I am hiring!
I have a fully funded PhD position available for someone with an interest in logic and statistics, at Delft University of Technology (Netherlands).
Application deadline: 31 August 2025
#AcademicJobs #AcademicMastodon #GetFediHired #AcademicJob #SymbolicAI #Statistics #AI #ConstraintProgramming #CombinatorialOptimisation #SensitivityAnalysis #FormalMethods #CombinatorialOptimization #Delft #TUDelft #AcademicChatter
My colleagues at TU Delft are seeking to hire a postdoc to work on Applied Planning and Scheduling under Uncertainty, with applications in modelling supply chain scenarios for offshore wind farm installation: https://careers.tudelft.nl/job/Delft-Postdoc-in-Applied-Planning-and-Scheduling-under-Uncertainty-2628-CD/814890902/
#AcademicMastodon #PostdocLife #Hiring #Research #Planning #Scheduling #ReasoningUnderUncertainty #AI #ArtificialIntelligence #JobSearch #Vacancy #AcademicChatter #Career #CombinatorialOptimisation #Sustainability #EnergyTransition #Wind #WindEnergy #ReinforcementLearning #ML #MachineLearning #WindTurbines #ComputerScience #Optimisation #Optimization #CombinatorialOptimization #PostDoc #AcademicCareer #Academia #AcademicJob #AcademicJobs #TUDelft
✍️A benchmark for #quantum optimization: the #traveling salesman #by Richard H. Warren
🔗10.26421/QIC21.7-8-2
🚀 Exciting update! My Network Algorithms and Approximations course continues with Lessons 2, 3 & 4 now available! 🎉
📌 Topics covered:
✅ Submodular (Set) Cover – Greedy log-approximation & Group Steiner Tree
✅ Maximum Coverage – 1 - 1/e approx, LP relaxations & budgeted coverage
✅ Unique Coverage – Log(n)-approximation, NP-hardness & max-cut ties
🔗 Watch now:
▶️ Lesson 2: https://youtu.be/xi6P3bqy61g
▶️ Lesson 3: https://youtu.be/jC44JdD74Hw
▶️ Lesson 4: https://youtu.be/ypzFnl0Wfp4
📅 New lectures premiere every Wednesday at 7PM ET!
📺 Full playlist: https://www.youtube.com/playlist?list=PLx7SjCaKZzEIeJxOlTuXveAE5eY7WOYB9
🔔 Subscribe for more: https://www.youtube.com/@hajiaghayi
#Optimization #Algorithms #NetworkDesign #CombinatorialOptimization #MachineLearning #GraphTheory #SetCover #ApproximationAlgorithms
2/n Very much like this visualisation presented by Frank Phillipson (based on a fig from Caceres-Cruz et al., 2014) of solvers for optimisation problems. Might try to use this or something like it in the course on algorithm for NP-hard problems that I am involved in.
#Algorithms #Algorithmics #CombinatorialOptimisation #CombinatorialOptimization #Optimisation #Optimization #SIGAlgo #SIGAlgo24 #ComputerScience
Fully Programmable Spatial Photonic Ising Machine by Focal Plane Division
https://arxiv.org/abs/2410.10689
#IsingMaching #SpinGlasses #CombinatorialOptimization #Photonics #Optics
arXiv:2410.10689
Ising machines are an emerging class of hardware that promises ultrafast and energy-efficient solutions to NP-hard combinatorial optimization problems. Spatial photonic Ising machines (SPIMs) exploit optical computing in free space to accelerate the computation, showcasing parallelism, scalability, and low power consumption. However, current SPIMs can implement only a restricted class of problems. This partial programmability is a critical limitation that hampers their benchmark. Achieving full programmability of the device while preserving its scalability is an open challenge. Here, we report a fully programmable SPIM achieved through a novel operation method based on the division of the focal plane. In our scheme, a general Ising problem is decomposed into a set of Mattis Hamiltonians, whose energies are simultaneously computed optically by measuring the intensity on different regions of the camera sensor. Exploiting this concept, we experimentally demonstrate the computation with high success probability of ground-state solutions of up to 32-spin Ising models on unweighted maximum cut graphs with and without ferromagnetic bias. Simulations of the hardware prove a favorable scaling of the accuracy with the number of spins. Our fully programmable SPIM enables the implementation of many quadratic unconstrained binary optimization problems, further establishing SPIMs as a leading paradigm in non von Neumann hardware.
Ising machines are an emerging class of hardware that promises ultrafast and energy-efficient solutions to NP-hard combinatorial optimization problems. Spatial photonic Ising machines (SPIMs) exploit optical computing in free space to accelerate the computation, showcasing parallelism, scalability, and low power consumption. However, current SPIMs can implement only a restricted class of problems. This partial programmability is a critical limitation that hampers their benchmark. Achieving full programmability of the device while preserving its scalability is an open challenge. Here, we report a fully programmable SPIM achieved through a novel operation method based on the division of the focal plane. In our scheme, a general Ising problem is decomposed into a set of Mattis Hamiltonians, whose energies are simultaneously computed optically by measuring the intensity on different regions of the camera sensor. Exploiting this concept, we experimentally demonstrate the computation with high success probability of ground-state solutions of up to 32-spin Ising models on unweighted maximum cut graphs with and without ferromagnetic bias. Simulations of the hardware prove a favorable scaling of the accuracy with the number of spins. Our fully programmable SPIM enables the implementation of many quadratic unconstrained binary optimization problems, further establishing SPIMs as a leading paradigm in non von Neumann hardware.
GHOST v3 is released! It now integrates a complete solver searching for all possible solutions of your combinatorial problems.
The interface also slightly changed, but you need to change one method name only in your current code: Solver::solve becomes Solver::fast_search.
Some functionalities remain to be implemented, like the parallelization of this complete solver. Stay tuned!
https://github.com/richoux/GHOST
#ConstraintProgramming #CombinatorialOptimization #AI #Framework
“#7AYW #Day3 #CombinatorialOptimization + #MachineLearning Nuria Gómez-Vargas @justnuu_ shows a predict-and-optimize approach to guide the training of ML models with performances on the optimization problem and to enhance sparsity in the feature space for decisions explainability.”
Dr. Anna Latour
QIC Journal
Mohammad Hajiaghayi
Claudio Conti
Florian Richoux
Ulrich Junker
AIROYoung