Internship Graduate-Level Research in Industrial Projects for Students GRIPS

Are you a master or PhDstudent and looking for an exciting fully funded research internship this summer Do you want to apply your skills to realworld industrial challenges while working in an international team The GraduateLevel Research in Industrial Projects for Students (GRIPS) Berlin is your opportunity to collaborate on cuttingedge industrysponsored research at the Research Campus MODAL located at the Zuse Institute Berlin (ZIB). Over eight weeks youll work in crosscultural teams tackling highimpact problems in both analytical and computational research. (See list of projects below.The program will run from June 23 August 15 2025.GRIPS is a joint activity of ZIB (Research Campus MODAL) and IPAM (Institute for Pure & Applied Mathematics (at) UCLA).TasksAs a GRIPS participant youll: Work in international crosscultural teams with students from the U.S. and Europe. Solve challenging industrysponsored research problems gaining valuable realworld experience. (Please see the list of this years projects below. You can only work in ONE project during the internship. Conduct both analytical and computational research applying mathematical and datadriven approaches.RequirementsEligible applicants include master and PhD students from the areas of mathematics computer or natural sciences (and related fields) who are currently enrolled at a European university. Due to the large number of applications we typically receive we do not accept applications from previous RIPS or GRIPS students.Please submit the following documents :motivation (cover) letter (please state your preferred project)resume/CVan academic record or transcript (can be unofficial).Apply now and secure your spot in this fully funded international research internship!BenefitsCosts for traveling and accommodation in Berlin are covered.Here is the list of the 2025 GRIPS projects. You can apply for one of these projects only. Please state your preferred project in the motivation letter.Project 1: Matrix Inspection Tool for Scientific ComputingIndustry Partner: FICOProject Description: In many scientific fields especially those reliant on scientific computing analyzing matrices is a fundamental aspect of research. However this process can be cumbersome and an effective matrix visualization tool has the potential to significantly expedite it. Moreover floatingpoint arithmetic is a common source of errors particularly when computations involve numbers with vastly different magnitudes making it essential to identify rows and columns with problematic numerical behavior. A naive implementation that simply colors nonzero entries will not be informative when dealing with very sparse matrices which are common in many applications. Additionally the tool should be capable of detecting common structural patterns; for example it is not unusual for a row or column to be expressed as the sum of other rows or columns and recognizing these aggregated representations can lead to a smaller and more manageable problem formulations. One area where this tool could be beneficial is MixedInteger Programming as such visualizations can expose structural patterns and highlight the numerical properties of matrices that lead to suboptimalalgorithm performance.Methodology:Build a visualization for large matrices.Getting a rowbyrow/columnbycolumn analysis of the numerics the coefficient range the number of nonzeros etc.Highlighting columns and rows with unfriendly numerical properties.Build a userfriendly interface.Detecting interesting domainspecific structures within the matrices.Expected outcomes:An easytouse open source tool allowing for a quick visual inspection of matrices and some of their statistics. The tool will also automatically detect common structures.Requirements of applicants:Interest in working with state of the art numerical tools.Strong programming skills with experience in NumPy Pandas or similar libraries.Experience in data analysis.Supervision and Collaboration:This project is supervised by MODAL SynLab in collaboration with the industry partner FICO a leader in optimization solvers for mixedinteger programming.Project 2: TopologyAware NonRigid Registration for Medical and Vision ApplicationsIndustry Partner: Stryker (formerly: 1000shapes GmbH)Project Description: Nonrigid registration under topological changes is a crucial research area in computer vision and graphics with significant applications spanning various domains. This problem involves aligning a deformable template with an observed 2D or 3D data while accounting for topological modifications such as splitting or merging of structures. This research is particularly relevant in medical imaging environmental monitoring biological sciences and robotics.We will investigate multiple optimization techniques that are prevalent in the nonrigid registration literature and adapt these methods to handle topological changes while utilizing geometric properties of the template to constrain the search space.Methodology: The primary challenge in nonrigid registration involving topological changes is the strategic application of priors and geometric constraints to effectively attain global minima in what is inherently a highly illposed problem. Given a template shape the project aims to:1. Investigate solutions for nonrigidly registering the shape to pointclouds and projection of pointclouds with globally optimal solutions the deformation being constrained by differential geometric properties2. Investigate utilizing discrete differential geometry along with suitable convex relaxations for nonrigid registration3. Determine methods for detecting topological changes and localizing separation boundaries when they exist4. Modifying shapes to adapt to the new topology detected in the previous stepExpected Outcome:A novel method for modifying the topology of shapes based on 2D and 3D cuesRequirements for the Applicants: We invite applications from highly motivated graduate students with a strong background in mathematics computer vision or related fields. The ideal candidate should meet the following criteria:Programming skills in MATLAB and/or PythonFamiliarity with optimization algorithms including convex and nonconvex optimizationFamiliarity with fundamentals of computer visionFamiliarity with basic differential geometryAbility to work independently and collaboratively in a multidisciplinary research environmentProject 3: Learning to create railway disposition conceptsIndustry Partner: DB InfraGO AGProject Description: Delays disturbances and disruptions are the daily business of railway infrastructure managers and operators. While modern optimization methods are welldeveloped for strategic and operational planning tasks realtime management often lies solely in the hand of experienced dispatchers. In the MODAL MobilityLab we want to bridge this gap. A simple idea to deal with the time criticality is to precompute disposition concepts. This allows in principle to apply mathematical models that are targeted at planning but those are typically computationally challenging and they do not necessarily follow the typical disposition philosophy. Instead of a global system optimum dispatchers prefer to follow a concept that sticks as much as possible to the regular timetable and differs only in a very limited number of places. It is therefore natural to think about disposition actions such as shortturning and changing train orders track allocations or dwell times and to create an optimized disposition concept that applies a selection of these actions. The challenge is to find out which of these actions are necessary impactful and improving. Speaking in mathematical terms we want to find good quality solutions to a hard combinatorial optimization problem by using a multiagent reinforcement learning approach. The project targets at first at understanding how to model the railway disposition concept optimization problem in terms of mixedinteger linear programming then to define suitable agents and their actions and finally to implement and evaluate learning strategies on data provided by DB InfraGO AG Germanys largest railway infrastructure manager.Requirements for the applicants:Familiarity with programming in PythonBasic knowledge of algorithmic discrete mathematics or machine learningInterest in implementing dataintensive algorithms to solve practical problems in mobilityAbility to work independently and as part of a team Key Skills Anti Money Laundering,Illustration,Access Control System,Drafting,Food Processing,Data Analysis Employment Type : Full Time Experience: years Vacancy: 1