5 Constraint Programming Jobs

Are you passionate about harnessing the power of artificial intelligence to transform decision-making under uncertainty Do you aspire to develop innovative algorithms that can offer robust and reliable recommendations for critical real-world problems If your academic ambitions lie at the intersection of symbolic AI, optimization, and statistical reasoning, this PhD position at TU Delft could be your gateway to cutting-edge research and impactful career opportunities. Strong and confident decision-making is at the core of many societal and engineering challenges today. Yet, the complexity and uncertainty inherent in real-world systems often make it difficult to ascertain which variables truly influence outcomes. This PhD project invites you to leverage advanced combinatorial and algorithmic methods to identify these critical parameters, thus enabling more robust, data-efficient, and reliable decision-making processes. Delft University of Technology (TU Delft) is one of Europe's premier technical universities, renowned for its pioneering research, high-impact innovation, and vibrant academic community. Located in the historic city of Delft, the university boasts a strong tradition in engineering, science, and design, with a global reputation for excellence. TU Delft has been at the forefront of groundbreaking advancements, from the world-famous Dutch waterworks to state-of-the-art biotech and digital technologies. As a PhD candidate at TU Delft, you will join a diverse and international research environment, collaborating with leading experts and contributing to solutions for some of society's most pressing challenges. The focus of this PhD position is on Symbolic AI and Reasoning Under Uncertainty, with a particular emphasis on sensitivity analysis for decision-making models. In contemporary applications, algorithms generate recommendations based on complex models with numerous input parameters. However, determining which of these inputs genuinely drive the outcomes is crucial for resource allocation, contingency planning, and ensuring the robustness of decisions. Traditional sensitivity analysis often relies on computationally intensive simulations, which may overlook significant variables. This project proposes a paradigm shift: employing logic-based and structural methods to formally identify critical parameters. By exploiting the structure of logic problems, the research aims to deliver formal guarantees on robustness, reduce computational costs, and improve the reliability of decision-support systems. This approach holds immense promise for fields such as engineering, policy-making, and societal infrastructure, where robust and explainable AI is essential. As a PhD candidate, you will be part of the Algorithmics section within the Software Technology (ST) department at TU Delft, collaborating closely with the Statistics section of the Delft Institute of Applied Mathematics (DIAM). The project is supervised by a distinguished team: dr. ir. Sicco Verwer (promotor), dr. Fabian Mies, and dr. Anna Latour (co-promotores). Your research will build upon and expand knowledge in modeling and solving paradigms such as Boolean Satisfiability, Constraint Programming, and Mixed-Integer (Linear) Programming. You will also develop methods for reasoning about discrete probability distributions, sampling complex spaces, and statistical analysis. The role involves collaboration with other researchers and software engineers to create practical tools for both academic and societal stakeholders. You will disseminate your findings through publications and presentations at leading conferences and journals, providing excellent opportunities for international networking and professional growth. The Algorithmics section is a dynamic, diverse environment where PhD and postdoc researchers work together on theoretical and algorithmic contributions to intelligent decision-making. The group addresses challenges including scalability, model learning for planning and verification, and the integration of stakeholder preferences into algorithmic processes. TU Delft's interdisciplinary ecosystem further enhances your research experience, with opportunities to engage in cross-faculty initiatives addressing topics such as climate change, energy transition, and artificial intelligence. Applicants should possess a Master's degree in Computer Science, Operations Research, or a related field (required by the Graduate School of TU Delft), proficiency in the English language, demonstrable knowledge of reasoning paradigms such as Boolean Satisfiability, Constraint Programming, or Mixed Integer (Linear) Programming, programming skills in languages such as Java, Python, or C++, solid understanding of statistics and probability theory, excellent critical and analytical thinking skills, a proven record of and interest in further developing research skills, including self-organization, academic writing, and a critical attitude, strong enthusiasm for state-of-the-art algorithmic and optimization techniques, especially their application to decision-making, affinity for teaching and mentoring students, and ability to work effectively in a team and take initiative. This position is ideal for candidates who are passionate about symbolic AI, optimization, statistical reasoning, and who are eager to contribute to the advancement of robust decision-making methodologies. If you are driven to make a meaningful impact on decision-making methodologies and thrive in a collaborative, interdisciplinary environment, we encourage you to apply for this PhD position at TU Delft.,

As an Optimization Specialist, you will be responsible for building and scaling mathematical models and process optimization. Your role will involve designing and implementing optimization models such as LP, MIP, and MILP for real-world scheduling, routing, and planning scenarios. Additionally, you will be expected to construct simulation models to evaluate performance under uncertainty, collaborate with cross-functional teams to refine problem definitions, and translate them into quantitative models. You will work closely with data scientists, software engineers, and business analysts to drive predictive optimization workflows using AI/ML algorithms for forecasting, clustering, and classification. Conducting what-if analyses and sensitivity testing to support decision-making will be a crucial part of your responsibilities. Furthermore, you will support research and pilot initiatives involving Quantum-Inspired Optimization (QUBO, hybrid models) and present findings and model performance to stakeholders through clear and concise visualizations and documentation. To excel in this role, you must possess a strong knowledge of Linear Programming (LP), Mixed Integer Programming (MIP/MILP), Constraint Programming, and have experience formulating and solving large-scale combinatorial problems. Hands-on experience with Optimization libraries like Pyomo, PuLP, and solvers such as CPLEX, Gurobi, GLPK, or CBC will be beneficial. Proficiency in programming languages like Python, R, or MATLAB is essential, along with simulation expertise in tools like SimPy, AnyLogic, Arena, or equivalent for discrete-event or agent-based simulation. Your educational background should include a PhD in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or a related field. Additionally, applied machine learning knowledge including forecasting models, clustering, and model evaluation metrics (e.g., MAPE, RMSE) will be advantageous. Strong communication and documentation skills are necessary to effectively present models and insights to non-technical audiences.,

We are looking for an Optimization Specialist who will be responsible for developing and deploying advanced analytical solutions leveraging mathematical optimization techniques. As an Optimization Specialist, you will have a crucial role in shaping the future of manufacturing operations by designing, developing, and deploying innovative scheduling solutions to optimize complex business processes. The ideal candidate for this position will excel in formulating and solving various optimization problems such as LP, MILP, combinatorial optimization, and scheduling. You should be adept at applying innovative algorithms to achieve optimal solutions. In this role, you will be working with a technical environment that includes language Python, frameworks like COIN-OR, PyTorch, NumPy, SciPy, Pyomo, and Django, application architecture that is scalable, resilient, reactive, event-driven, and secure microservices architecture, and cloud services provided by AWS. Your responsibilities will include designing, developing, and implementing advanced mathematical optimization models and solvers to address complex business challenges across various domains. You will identify and model optimization problems in different manufacturing businesses, design and implement robust optimization algorithms, evaluate and select appropriate solvers, and develop machine learning models to support data-driven decision-making. To qualify for this position, you should have 1-3 years of experience in Operations Research and Python programming, a Bachelor's degree in Operations Research, Applied Mathematics, Statistics, Computer Science, Data Science, or a related field, experience in building optimization models from scratch, knowledge of constraint programming, and proficiency in statistical analysis tools such as Python. Strong problem-solving abilities, creativity, and effective communication skills are essential. Join our team at Techtinium, where we have a passion for innovative products and a commitment to delivering superior software solutions. We value our team members and provide a positive, collaborative environment where your skills will be honed across the software development lifecycle. If you are ready for an exciting professional adventure, explore opportunities with us at https://techtinium.com/. We are eager to welcome you to our team!,

We are looking for an Optimization Specialist who will be responsible for developing and deploying advanced analytical solutions using mathematical optimization techniques. In this role, you will be instrumental in shaping the future of manufacturing operations by creating innovative scheduling solutions that optimize complex business processes. As the ideal candidate, you should excel in formulating and solving various optimization problems such as LP, MILP, combinatorial optimization, and scheduling. You should be proficient in applying innovative algorithms to achieve optimal solutions. Your primary responsibilities will include designing, developing, and implementing advanced mathematical optimization models and solvers to address complex business challenges in areas like scheduling, routing, resource allocation, and inventory management. You will also be involved in identifying and modeling optimization problems across different manufacturing businesses and implementing robust optimization algorithms. In addition to this, you will be required to design, develop, and deploy machine learning models to analyze large datasets and generate insights that support data-driven decision-making, specifically related to enhancing the performance of optimization algorithms. You will also need to keep abreast of the latest developments in optimization and computational intelligence and incorporate these advancements into problem-solving strategies. The qualifications we are looking for in a candidate include 1-3 years of experience in Operations Research and Python programming, a Bachelor's degree in Operations Research, Applied Mathematics, Statistics, Computer Science, Data Science, or a related field, experience in building optimization models from scratch, knowledge of constraint programming, and experience with both non-commercial and commercial optimization solvers. Strong statistical analysis skills and effective communication skills are also essential for this role. Join Techtinium, a company passionate about innovative products, where you will be part of a diverse team dedicated to delivering superior software solutions. At Techtinium, your skills will be honed across the entire software development lifecycle in a positive, thought-provoking, and collaborative environment. If you are ready for an exciting professional adventure, explore opportunities with us at https://techtinium.com/. We look forward to welcoming you to our team!,

We are seeking a QA Engineer with expertise in system testing , and automated test frameworks to validate and ensure the accuracy of Variant Solver algorithms . The ideal candidate will have experience in constraint programming , solver validation , and performance bench marking . Key Responsibilities: Design and implement unit tests to validate individual components of the Variant Solver . Conduct system testing to ensure the solver functions correctly in integrated environments. Develop automated test scripts using JUnit or TestNG . Identify edge cases and create comprehensive test scenarios for constraint-solving algorithms . Collaborate with developers to refine solver accuracy and optimize performance. Perform regression testing to ensure solver updates do not introduce defects. Analyze test results and provide detailed reports on solver behavior and efficiency. Stay updated with the latest advancements in solver testing methodologies . Requirements: BE or BTech, 3 to 6 years of experience in QA testing for algorithm-based applications. Strong proficiency in Java, Python, or C++ for test automation. Hands-on experience with unit testing frameworks like JUnit, PyTest, or TestNG . Knowledge of constraint solvers such as Z3 Solver, Choco Solver, or Google OR-Tools . Experience in performance testing and benchmarking solver efficiency . Ability to work collaboratively with developers and researchers. Strong analytical and problem-solving skills. Experience contributing to open-source projects related to solver testing. Familiarity with formal verification and symbolic reasoning . Understanding of parallel computing and high-performance solver implementations . Knowledge of graph algorithms and heuristic search methods .