#constraintprogramming — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #constraintprogramming, aggregated by home.social.
-
So the problem isn't routing specifically, the problem is that optimization takes forever. Why is *that* happening?
My data structure is organized as a N-ary tree, where each non-leaf node is an AND or OR. AND means everything underneath has to be schedule or not scheduled. OR means exactly one child must be scheduled and nothing else.
I'm allowing third-party clients to send me trees and then I combine them all to send to the scheduler.
But I was combining them as a big AND.
This requires the solver to find a place for everything. It wants to be efficient, so it isn't going to start with a few things and add them in--what if it ends up being unable to do that? So it dumps everything on the board and then tries to do 1-2 NP-hard algorithms on it.
If I combine the top level under a special code that allows just the next level of children either be in or out, everything runs so much better.
Now the solver can construct some good sub-solution and add to it. I get fast routing answers! I see the best solution growing smoother!
AND I just ran it with my latest *extremely simple* TSP constraints and got such a nice routing graph in only 30s.
I mighta done did it!
-
So the problem isn't routing specifically, the problem is that optimization takes forever. Why is *that* happening?
My data structure is organized as a N-ary tree, where each non-leaf node is an AND or OR. AND means everything underneath has to be schedule or not scheduled. OR means exactly one child must be scheduled and nothing else.
I'm allowing third-party clients to send me trees and then I combine them all to send to the scheduler.
But I was combining them as a big AND.
This requires the solver to find a place for everything. It wants to be efficient, so it isn't going to start with a few things and add them in--what if it ends up being unable to do that? So it dumps everything on the board and then tries to do 1-2 NP-hard algorithms on it.
If I combine the top level under a special code that allows just the next level of children either be in or out, everything runs so much better.
Now the solver can construct some good sub-solution and add to it. I get fast routing answers! I see the best solution growing smoother!
AND I just ran it with my latest *extremely simple* TSP constraints and got such a nice routing graph in only 30s.
I mighta done did it!
-
Is it possible the solution was right there all along??
I've been on-and-off tweaking the #radar #scheduling #optimization #constraintprogramming #python #software project to include routing
I kept finding that routing takes longer to figure out than pure packing--no surprise.
But this was even the case when the constraints put on the routing were extremely minimal.
This made me wonder how much actual optimization was happening in the NON-routing case. As it turns out, none. If it doesn't have to do any work, it goes fast.
Sounds pretty obvious, but it's very helpful. Why? See next toot.
-
Is it possible the solution was right there all along??
I've been on-and-off tweaking the #radar #scheduling #optimization #constraintprogramming #python #software project to include routing
I kept finding that routing takes longer to figure out than pure packing--no surprise.
But this was even the case when the constraints put on the routing were extremely minimal.
This made me wonder how much actual optimization was happening in the NON-routing case. As it turns out, none. If it doesn't have to do any work, it goes fast.
Sounds pretty obvious, but it's very helpful. Why? See next toot.
-
Is it possible the solution was right there all along??
I've been on-and-off tweaking the #radar #scheduling #optimization #constraintprogramming #python #software project to include routing
I kept finding that routing takes longer to figure out than pure packing--no surprise.
But this was even the case when the constraints put on the routing were extremely minimal.
This made me wonder how much actual optimization was happening in the NON-routing case. As it turns out, none. If it doesn't have to do any work, it goes fast.
Sounds pretty obvious, but it's very helpful. Why? See next toot.
-
Is it possible the solution was right there all along??
I've been on-and-off tweaking the #radar #scheduling #optimization #constraintprogramming #python #software project to include routing
I kept finding that routing takes longer to figure out than pure packing--no surprise.
But this was even the case when the constraints put on the routing were extremely minimal.
This made me wonder how much actual optimization was happening in the NON-routing case. As it turns out, none. If it doesn't have to do any work, it goes fast.
Sounds pretty obvious, but it's very helpful. Why? See next toot.
-
Is it possible the solution was right there all along??
I've been on-and-off tweaking the #radar #scheduling #optimization #constraintprogramming #python #software project to include routing
I kept finding that routing takes longer to figure out than pure packing--no surprise.
But this was even the case when the constraints put on the routing were extremely minimal.
This made me wonder how much actual optimization was happening in the NON-routing case. As it turns out, none. If it doesn't have to do any work, it goes fast.
Sounds pretty obvious, but it's very helpful. Why? See next toot.
-
"It is important to choose the right tool for the job." 🤓
Using OR-Tools CP-SAT for Scheduling Problems https://atalaykutlay.com/or-tools-cp-sat-for-scheduling-problems.html #optimization #OperationsResearch #ConstraintProgramming
-
"It is important to choose the right tool for the job." 🤓
Using OR-Tools CP-SAT for Scheduling Problems https://atalaykutlay.com/or-tools-cp-sat-for-scheduling-problems.html #optimization #OperationsResearch #ConstraintProgramming
-
"It is important to choose the right tool for the job." 🤓
Using OR-Tools CP-SAT for Scheduling Problems https://atalaykutlay.com/or-tools-cp-sat-for-scheduling-problems.html #optimization #OperationsResearch #ConstraintProgramming
-
"It is important to choose the right tool for the job." 🤓
Using OR-Tools CP-SAT for Scheduling Problems https://atalaykutlay.com/or-tools-cp-sat-for-scheduling-problems.html #optimization #OperationsResearch #ConstraintProgramming
-
"It is important to choose the right tool for the job." 🤓
Using OR-Tools CP-SAT for Scheduling Problems https://atalaykutlay.com/or-tools-cp-sat-for-scheduling-problems.html #optimization #OperationsResearch #ConstraintProgramming
-
They liked the #constraintprogramming #radar #scheduling talk so much they want me to give it again to our sister group. Whee.
Meanwhile, when I named it I backronymed it to a name I wanted to use about 10 years ago.
My boss immediately asked if I also came up with a logo.
I'm using that question to justify spending the rest of the day designing some options in #inkscape ...
-
They liked the #constraintprogramming #radar #scheduling talk so much they want me to give it again to our sister group. Whee.
Meanwhile, when I named it I backronymed it to a name I wanted to use about 10 years ago.
My boss immediately asked if I also came up with a logo.
I'm using that question to justify spending the rest of the day designing some options in #inkscape ...
-
Thank the goddesses for my recent attempts at #music transcription and also for #adhd #rx
My #constraintprogramming #radar #scheduling talk is in *gulp* two weeks. My rough draft is due FRIDAY.
5 years ago, I spent a week in front of blank slides, sometimes in tears, unable to even conceive of a method by which I could start.
Earlier this week, I did at least manage to gather a lot of content slides from other decks but as of this morning it was all just a jumble.
This morning I took a two-pronged approach
1. Clear the runway: Do dumb things like checking my inbox, getting a drink and music squared away, etc first. THEN take meds and dive in as they ramp up.
2. I was having trouble getting started but also specifically having trouble with the start of the slides. But there's no reason the first slide in the deck has to be the first one created.
For the song I've been working on transcribing, I gave up on "note by note from the beginning" approach. Instead I found spots where it was easy to figure out (single sustained notes, simple melodic progressions, etc). Then I tied those together knowing where the mystery passage had to start and end.
Why not do the same thing with PowerPoint? I definitely know what I want to say in the "design proposal" section. And I think I know what some future work will be. Start with those parts!
That only took like 30 minutes. And with that out of the way, it looked a lot more do-able to find some points to make in the "how did we get here" section.
I now have a (very) rough draft of the talk!
-
Thank the goddesses for my recent attempts at #music transcription and also for #adhd #rx
My #constraintprogramming #radar #scheduling talk is in *gulp* two weeks. My rough draft is due FRIDAY.
5 years ago, I spent a week in front of blank slides, sometimes in tears, unable to even conceive of a method by which I could start.
Earlier this week, I did at least manage to gather a lot of content slides from other decks but as of this morning it was all just a jumble.
This morning I took a two-pronged approach
1. Clear the runway: Do dumb things like checking my inbox, getting a drink and music squared away, etc first. THEN take meds and dive in as they ramp up.
2. I was having trouble getting started but also specifically having trouble with the start of the slides. But there's no reason the first slide in the deck has to be the first one created.
For the song I've been working on transcribing, I gave up on "note by note from the beginning" approach. Instead I found spots where it was easy to figure out (single sustained notes, simple melodic progressions, etc). Then I tied those together knowing where the mystery passage had to start and end.
Why not do the same thing with PowerPoint? I definitely know what I want to say in the "design proposal" section. And I think I know what some future work will be. Start with those parts!
That only took like 30 minutes. And with that out of the way, it looked a lot more do-able to find some points to make in the "how did we get here" section.
I now have a (very) rough draft of the talk!
-
Or really, I should just rewrite this as a VRP, since it is so close to that anyway and the code I have was just a prototype.
#constraintprogramming #python #radar #orbit #scheduling #math #computerscience
-
Or really, I should just rewrite this as a VRP, since it is so close to that anyway and the code I have was just a prototype.
#constraintprogramming #python #radar #orbit #scheduling #math #computerscience
-
A true Traveling Salesman Problem on top of the Knapsack Problem takes longer than I want to spend (many minutes vs ~10s) and also does more than I want.
A forced-greedy TSP (near neighbors are all scored 1, farther nodes are all scored 0) is much faster and closer to what I want but still longer than I want (~2m)
The solution to a combinatorial problem is not a faster algorithm, it's a smaller problem.
I need to break the TSP into chunks and solve them separately. The difficult thing is that I need to do this while deliberately remaining unaware of the semantics of the nodes for larger architectural reasons.
#constraintprogramming #python #radar #orbit #scheduling #math #computerscience
-
A true Traveling Salesman Problem on top of the Knapsack Problem takes longer than I want to spend (many minutes vs ~10s) and also does more than I want.
A forced-greedy TSP (near neighbors are all scored 1, farther nodes are all scored 0) is much faster and closer to what I want but still longer than I want (~2m)
The solution to a combinatorial problem is not a faster algorithm, it's a smaller problem.
I need to break the TSP into chunks and solve them separately. The difficult thing is that I need to do this while deliberately remaining unaware of the semantics of the nodes for larger architectural reasons.
#constraintprogramming #python #radar #orbit #scheduling #math #computerscience
-
This radar scheduling thing is dominated by a Knapsack Problem with a dash of Traveling Salesman to keep from wearing out hardware/looking dumb.
I think I have a workable solution to each of these now.
It took a little while to get the TSP portion to behave usefully. It's funny, because you'd think getting a "feasible" solution would be easier than an optimal one. But in terms of software it's much more difficult--you have to define "feasible".
The TSP-relevant tasks are the GEO satellite belt. These objects are spread out in a mostly-linear ~150deg azimuthal band that is somewhat clustered. You might have 5 objects within a degree of each other and then nothing for 3 degrees.
Inside these clumps, I don't care about the path much. But I definitely don't want to zip back and forth between clumps.
I can't limit to a set time since other jobs may intervene. I can't do it as a percentage since I don't know what percentage these jobs are at ay given time.
What I ended up doing is basically defining the costs just like I described them above. We have "close' objects (within say 3 degrees) that all have the same cost. Then medium and and large distance.
For a problem with more time than jobs, I get a reasonable answer in 3s. With more jobs than time, I just set a 10s upper bound and still get a reasonable time.
I need to integrate this solution with the larger program to really check runtimes with a more realistic set of jobs. But I think I have enough understanding and knobs in place to make this work.
#constraintprogramming #ortools #scheduling/#routing #optimization #python #software #programming #engineering
-
This radar scheduling thing is dominated by a Knapsack Problem with a dash of Traveling Salesman to keep from wearing out hardware/looking dumb.
I think I have a workable solution to each of these now.
It took a little while to get the TSP portion to behave usefully. It's funny, because you'd think getting a "feasible" solution would be easier than an optimal one. But in terms of software it's much more difficult--you have to define "feasible".
The TSP-relevant tasks are the GEO satellite belt. These objects are spread out in a mostly-linear ~150deg azimuthal band that is somewhat clustered. You might have 5 objects within a degree of each other and then nothing for 3 degrees.
Inside these clumps, I don't care about the path much. But I definitely don't want to zip back and forth between clumps.
I can't limit to a set time since other jobs may intervene. I can't do it as a percentage since I don't know what percentage these jobs are at ay given time.
What I ended up doing is basically defining the costs just like I described them above. We have "close' objects (within say 3 degrees) that all have the same cost. Then medium and and large distance.
For a problem with more time than jobs, I get a reasonable answer in 3s. With more jobs than time, I just set a 10s upper bound and still get a reasonable time.
I need to integrate this solution with the larger program to really check runtimes with a more realistic set of jobs. But I think I have enough understanding and knobs in place to make this work.
#constraintprogramming #ortools #scheduling/#routing #optimization #python #software #programming #engineering
-
#constraintprogramming #ortools #scheduling/#routing #optimization
I think I may have gotten this working in the toy problem. I can get a list of time-ordered optional intervals AND a circuit in the same order. That will let me put sequential constraints at the circuit level (I hope)
The key turned out to be to make sure all present intervals were also entered nodes AND that every x->y edge meant that x.time < y.time....and that the reverse is not true!
It's weird how confused I am by half-reified constraints, given that's *exactly* how programming languages do variable assignment.
a = b
makes a take the value b, but not vice versa.
-
#constraintprogramming #ortools #scheduling/#routing #optimization
I think I may have gotten this working in the toy problem. I can get a list of time-ordered optional intervals AND a circuit in the same order. That will let me put sequential constraints at the circuit level (I hope)
The key turned out to be to make sure all present intervals were also entered nodes AND that every x->y edge meant that x.time < y.time....and that the reverse is not true!
It's weird how confused I am by half-reified constraints, given that's *exactly* how programming languages do variable assignment.
a = b
makes a take the value b, but not vice versa.
-
#constraintprogramming #ortools #scheduling/#routing #optimization
I think I'm thinking about this wrong.
My basic problem is scheduling #radar collection intervals in continuous time. There are many constraints on the intervals already and that's all working (by which I mean "running"--I'm not actually connected to a radar yet).
However, I also want to put a constraint on interval *transitions* to make sure we don't require the dish to move instantaneously.
AFAIK, the only way to constrain adjacent items like this is via a circuit. So I was going to add a "circuit overlay" to the model. Then I could say that the "edge" from radar dwell A to radar dwell B had to be a certain time distance apart or whatever.
I'm writing a tiny test program to see how that works and it isn't doing what I expect.
But in debugging that, I think I just realized my entire plan doesn't work. There's no reason the circuit overlay is going to come out in the same order as the intervals as laid out in time. That voids the entire plan!
Also, even though all the kids are home for #christmas none of the #nerd are awake for rubber-duckying
GRRRRRRRRRRRRRR
-
#constraintprogramming #ortools #scheduling/#routing #optimization
I think I'm thinking about this wrong.
My basic problem is scheduling #radar collection intervals in continuous time. There are many constraints on the intervals already and that's all working (by which I mean "running"--I'm not actually connected to a radar yet).
However, I also want to put a constraint on interval *transitions* to make sure we don't require the dish to move instantaneously.
AFAIK, the only way to constrain adjacent items like this is via a circuit. So I was going to add a "circuit overlay" to the model. Then I could say that the "edge" from radar dwell A to radar dwell B had to be a certain time distance apart or whatever.
I'm writing a tiny test program to see how that works and it isn't doing what I expect.
But in debugging that, I think I just realized my entire plan doesn't work. There's no reason the circuit overlay is going to come out in the same order as the intervals as laid out in time. That voids the entire plan!
Also, even though all the kids are home for #christmas none of the #nerd are awake for rubber-duckying
GRRRRRRRRRRRRRR
-
Finally getting back to the #constraintprogramming #ortools #scheduling/#routing #optimization problem
I have a minimal working implementation of both a server and a selection of agents at various levels of realism. Two other #software peeps are making two real agents to talk to my server
...and I'm realizing I need to bump up my game and get ahead of them...again
For most of the scheduling types/agents, routing is a non-issue. For a couple types/agents it is absolutely crucial. It's one of those that is being implemented by one of the #engineers. And if answers come back unrouted, a higher-level person is going to absolutely POUNCE and start advocating Bad Ideas
So I was looking into circuit constraints and there isn't a lot of info out there beyond the basics.
Until I found this wonderful tutorial
https://github.com/d-krupke/cpsat-primer?tab=readme-ov-file
It covers everything from the beginning to advanced and and even niche usage, including such important things as runtime parameters and timing. All in #python!
-
Finally getting back to the #constraintprogramming #ortools #scheduling/#routing #optimization problem
I have a minimal working implementation of both a server and a selection of agents at various levels of realism. Two other #software peeps are making two real agents to talk to my server
...and I'm realizing I need to bump up my game and get ahead of them...again
For most of the scheduling types/agents, routing is a non-issue. For a couple types/agents it is absolutely crucial. It's one of those that is being implemented by one of the #engineers. And if answers come back unrouted, a higher-level person is going to absolutely POUNCE and start advocating Bad Ideas
So I was looking into circuit constraints and there isn't a lot of info out there beyond the basics.
Until I found this wonderful tutorial
https://github.com/d-krupke/cpsat-primer?tab=readme-ov-file
It covers everything from the beginning to advanced and and even niche usage, including such important things as runtime parameters and timing. All in #python!
-
So....for this #constraintprogramming thing
I have a bunch of individual job getting put into the solver. I'm requerying everything from scratch on every iteration because things can change and being responsive to that is exactly what this project is about.
However, I also want to keep the near future in the schedule relatively stable for a number of reasons. Human factors, for instance. Also there's some race conditions that can happen when the current time is within the duration of a job. Like what if the job disappears? Or if something just barely more important arrives--how valuable is avoiding rocking the boat vs doing the optimal task?
Ideally I would just hold the jobs in the near future as a constant. However, based on past experience I doubt the feasibility of even identifying "the same jobs" on each iteration (cf: "things can change" and also Ship of Theseus).
I've come up with a scheme that tries to hold the *gaps* between jobs as a (near) constant instead. That has its own complications.
Maybe I should go back to the original problem of trying to make the jobs identifiable even if they change....?
But that's not just hard, it also makes it the problem of a #software #developer in the future. (I'm not being thoughtful here, I'm being pessimistic that they'll do it right)
I guess I'll just Do The Best I Can on my end and make sure the users have the ability to control the rest.
Ugh.
-
So....for this #constraintprogramming thing
I have a bunch of individual job getting put into the solver. I'm requerying everything from scratch on every iteration because things can change and being responsive to that is exactly what this project is about.
However, I also want to keep the near future in the schedule relatively stable for a number of reasons. Human factors, for instance. Also there's some race conditions that can happen when the current time is within the duration of a job. Like what if the job disappears? Or if something just barely more important arrives--how valuable is avoiding rocking the boat vs doing the optimal task?
Ideally I would just hold the jobs in the near future as a constant. However, based on past experience I doubt the feasibility of even identifying "the same jobs" on each iteration (cf: "things can change" and also Ship of Theseus).
I've come up with a scheme that tries to hold the *gaps* between jobs as a (near) constant instead. That has its own complications.
Maybe I should go back to the original problem of trying to make the jobs identifiable even if they change....?
But that's not just hard, it also makes it the problem of a #software #developer in the future. (I'm not being thoughtful here, I'm being pessimistic that they'll do it right)
I guess I'll just Do The Best I Can on my end and make sure the users have the ability to control the rest.
Ugh.
-
My #constraintprogramming #programming journey has been extremely interesting
In just a couple weeks (using google or-tools, but this isn't an endorsement) I've been able to make reasonable schedules
(the existing "schedule" example is helpful to learn the tool but essentially completely unlike the scheduling problem I'm doing. it's more knapsack-y but the example for that was only slightly more helpful.)
Building this much was really only supposed to be ammo to help me defend against some really bad ideas in an upcoming meeting. But it works so well it will almost certainly become the core of the future system.
My question is: To a zeroth order this is a knapsack problem. To a first order it is *also* a traveling salesman problem.
How dumb would it be to add TSP as a soft constraint? Pretty dumb, right?
Except I really do want to do it because that effect is extremely noticeable in some circumstances. Can I figure out how to limit that constraint to only situations where it applies?
-
My #constraintprogramming #programming journey has been extremely interesting
In just a couple weeks (using google or-tools, but this isn't an endorsement) I've been able to make reasonable schedules
(the existing "schedule" example is helpful to learn the tool but essentially completely unlike the scheduling problem I'm doing. it's more knapsack-y but the example for that was only slightly more helpful.)
Building this much was really only supposed to be ammo to help me defend against some really bad ideas in an upcoming meeting. But it works so well it will almost certainly become the core of the future system.
My question is: To a zeroth order this is a knapsack problem. To a first order it is *also* a traveling salesman problem.
How dumb would it be to add TSP as a soft constraint? Pretty dumb, right?
Except I really do want to do it because that effect is extremely noticeable in some circumstances. Can I figure out how to limit that constraint to only situations where it applies?
-
This paper compares MILP and CP solvers on a new FJS scheduling problem, showing CP is faster and "warm starts" are vital for large instances. https://hackernoon.com/the-tortoise-and-the-hare-an-unexpected-scheduling-race-between-milp-and-cp-solvers #constraintprogramming
-
This paper compares MILP and CP solvers on a new FJS scheduling problem, showing CP is faster and "warm starts" are vital for large instances. https://hackernoon.com/the-tortoise-and-the-hare-an-unexpected-scheduling-race-between-milp-and-cp-solvers #constraintprogramming
-
A performance showdown for Job Shop scheduling. This study pits MILP, CP, and new heuristics against a fresh set of challenging problem instances. https://hackernoon.com/pitting-heuristics-against-exact-solvers-in-the-flexible-job-shop-gauntlet #constraintprogramming
-
A performance showdown for Job Shop scheduling. This study pits MILP, CP, and new heuristics against a fresh set of challenging problem instances. https://hackernoon.com/pitting-heuristics-against-exact-solvers-in-the-flexible-job-shop-gauntlet #constraintprogramming
-
This article formalizes DAG-based FJS with position-based learning via a MILP using position variables plus a CP Optimizer model. https://hackernoon.com/algorithms-that-learn-as-they-schedule-a-twin-model-approach-to-modern-fjs #constraintprogramming
-
This article formalizes DAG-based FJS with position-based learning via a MILP using position variables plus a CP Optimizer model. https://hackernoon.com/algorithms-that-learn-as-they-schedule-a-twin-model-approach-to-modern-fjs #constraintprogramming
-
Only a few days left to apply!
If you are interested in logic, decision-making, reasoning under uncertainty and statistics, apply by the end of this month for an opportunity to work with me, dr. Sicco Verwer and dr. Fabian Mies at Delft University of Technology!
Application deadline: 31 August 2025
#AcademicJobs
#AcademicMastodon
#GetFediHired
#AcademicJob
#SymbolicAI
#Statistics
#AI
#ConstraintProgramming
#CombinatorialOptimisation
#SensitivityAnalysis
#FormalMethods
#CombinatorialOptimization
#Delft
#TUDelft
#AcademicChatter -
Only a few days left to apply!
If you are interested in logic, decision-making, reasoning under uncertainty and statistics, apply by the end of this month for an opportunity to work with me, dr. Sicco Verwer and dr. Fabian Mies at Delft University of Technology!
Application deadline: 31 August 2025
#AcademicJobs
#AcademicMastodon
#GetFediHired
#AcademicJob
#SymbolicAI
#Statistics
#AI
#ConstraintProgramming
#CombinatorialOptimisation
#SensitivityAnalysis
#FormalMethods
#CombinatorialOptimization
#Delft
#TUDelft
#AcademicChatter -
Only a few days left to apply!
If you are interested in logic, decision-making, reasoning under uncertainty and statistics, apply by the end of this month for an opportunity to work with me, dr. Sicco Verwer and dr. Fabian Mies at Delft University of Technology!
Application deadline: 31 August 2025
#AcademicJobs
#AcademicMastodon
#GetFediHired
#AcademicJob
#SymbolicAI
#Statistics
#AI
#ConstraintProgramming
#CombinatorialOptimisation
#SensitivityAnalysis
#FormalMethods
#CombinatorialOptimization
#Delft
#TUDelft
#AcademicChatter -
Only a few days left to apply!
If you are interested in logic, decision-making, reasoning under uncertainty and statistics, apply by the end of this month for an opportunity to work with me, dr. Sicco Verwer and dr. Fabian Mies at Delft University of Technology!
Application deadline: 31 August 2025
#AcademicJobs
#AcademicMastodon
#GetFediHired
#AcademicJob
#SymbolicAI
#Statistics
#AI
#ConstraintProgramming
#CombinatorialOptimisation
#SensitivityAnalysis
#FormalMethods
#CombinatorialOptimization
#Delft
#TUDelft
#AcademicChatter -
Only a few days left to apply!
If you are interested in logic, decision-making, reasoning under uncertainty and statistics, apply by the end of this month for an opportunity to work with me, dr. Sicco Verwer and dr. Fabian Mies at Delft University of Technology!
Application deadline: 31 August 2025
#AcademicJobs
#AcademicMastodon
#GetFediHired
#AcademicJob
#SymbolicAI
#Statistics
#AI
#ConstraintProgramming
#CombinatorialOptimisation
#SensitivityAnalysis
#FormalMethods
#CombinatorialOptimization
#Delft
#TUDelft
#AcademicChatter -
One month left to apply!
If you are looking for a PhD position and are interested in working on probabilistic inference, sensitivity analysis, and decision-making, this might be the job for you! We are looking for candidates with a strong background in Computer Science, and ideally also in Mathematics.
Please apply by 31 August. We're looking forward to reading your application!
#AcademicJobs
#AcademicMastodon
#GetFediHired
#AcademicJob
#SymbolicAI
#Statistics
#AI
#ConstraintProgramming
#CombinatorialOptimisation
#SensitivityAnalysis
#FormalMethods
#CombinatorialOptimization
#Delft
#TUDelft
#AcademicChatter -
One month left to apply!
If you are looking for a PhD position and are interested in working on probabilistic inference, sensitivity analysis, and decision-making, this might be the job for you! We are looking for candidates with a strong background in Computer Science, and ideally also in Mathematics.
Please apply by 31 August. We're looking forward to reading your application!
#AcademicJobs
#AcademicMastodon
#GetFediHired
#AcademicJob
#SymbolicAI
#Statistics
#AI
#ConstraintProgramming
#CombinatorialOptimisation
#SensitivityAnalysis
#FormalMethods
#CombinatorialOptimization
#Delft
#TUDelft
#AcademicChatter -
One month left to apply!
If you are looking for a PhD position and are interested in working on probabilistic inference, sensitivity analysis, and decision-making, this might be the job for you! We are looking for candidates with a strong background in Computer Science, and ideally also in Mathematics.
Please apply by 31 August. We're looking forward to reading your application!
#AcademicJobs
#AcademicMastodon
#GetFediHired
#AcademicJob
#SymbolicAI
#Statistics
#AI
#ConstraintProgramming
#CombinatorialOptimisation
#SensitivityAnalysis
#FormalMethods
#CombinatorialOptimization
#Delft
#TUDelft
#AcademicChatter -
One month left to apply!
If you are looking for a PhD position and are interested in working on probabilistic inference, sensitivity analysis, and decision-making, this might be the job for you! We are looking for candidates with a strong background in Computer Science, and ideally also in Mathematics.
Please apply by 31 August. We're looking forward to reading your application!
#AcademicJobs
#AcademicMastodon
#GetFediHired
#AcademicJob
#SymbolicAI
#Statistics
#AI
#ConstraintProgramming
#CombinatorialOptimisation
#SensitivityAnalysis
#FormalMethods
#CombinatorialOptimization
#Delft
#TUDelft
#AcademicChatter -
One month left to apply!
If you are looking for a PhD position and are interested in working on probabilistic inference, sensitivity analysis, and decision-making, this might be the job for you! We are looking for candidates with a strong background in Computer Science, and ideally also in Mathematics.
Please apply by 31 August. We're looking forward to reading your application!
#AcademicJobs
#AcademicMastodon
#GetFediHired
#AcademicJob
#SymbolicAI
#Statistics
#AI
#ConstraintProgramming
#CombinatorialOptimisation
#SensitivityAnalysis
#FormalMethods
#CombinatorialOptimization
#Delft
#TUDelft
#AcademicChatter -
Hi everyone,
I feel like a re-introduction is long overdue!
My name is Anna, and I'm an assistant professor of Algorithmics at the Delft University of Technology, specialising in combinatorial optimisation, symbolic AI, constraint programming, propositional model counting, operations research and reasoning under uncertainty.
I'm a nerd, a feminist and a traveller, not always in that order.
In my spare time I like to hike and go geocaching. I try to go swing dancing a few times a week. I am a Trekkie. I want to learn how to draw. I am an Indomie and Obsidian enthusiast. Based in the Netherlands, I miss Belgium, Canada and Singapore.
Since a job in academia somehow always is personal, I have chosen to mix professional interactions with the more personal ones on this platform. At least for now. Obviously, my opinions do not necessarily reflect those of my employer yadiyadiyada.
Hope to keep interacting with you all!
#Introduction #AcademicMastodon #Algorithmics #SymbolicAI #CombinatorialOptimisation #ConstraintProgramming #ModelCounting #OperationsResearch #ProbabilisticInference #Geocaching #LindyHop #Jazz #SwingDancing #Hiking #Obsidian #StarTrek #Travel #TUDelft #MastoMiGoreng #Indomie #GNUTerryPratchett #Catstodon #Mastocats #Caturday #ExpatLife #MakanApaToda
-
Hi everyone,
I feel like a re-introduction is long overdue!
My name is Anna, and I'm an assistant professor of Algorithmics at the Delft University of Technology, specialising in combinatorial optimisation, symbolic AI, constraint programming, propositional model counting, operations research and reasoning under uncertainty.
I'm a nerd, a feminist and a traveller, not always in that order.
In my spare time I like to hike and go geocaching. I try to go swing dancing a few times a week. I am a Trekkie. I want to learn how to draw. I am an Indomie and Obsidian enthusiast. Based in the Netherlands, I miss Belgium, Canada and Singapore.
Since a job in academia somehow always is personal, I have chosen to mix professional interactions with the more personal ones on this platform. At least for now. Obviously, my opinions do not necessarily reflect those of my employer yadiyadiyada.
Hope to keep interacting with you all!
#Introduction #AcademicMastodon #Algorithmics #SymbolicAI #CombinatorialOptimisation #ConstraintProgramming #ModelCounting #OperationsResearch #ProbabilisticInference #Geocaching #LindyHop #Jazz #SwingDancing #Hiking #Obsidian #StarTrek #Travel #TUDelft #MastoMiGoreng #Indomie #GNUTerryPratchett #Catstodon #Mastocats #Caturday #ExpatLife #MakanApaToda