Job vacancies

25 PhD and Postdoc positions starting in summer 2024

We have recently closed a call for collaborative projects and will soon open a number of calls for PhD and Postdoc positions to start in summer 2024. The following projects were accepted:

• Multi-agent control with limited interpersonal comparability: Bolognani, Nax

• Karma economies: Bolognani, Censi 

• Control-oriented Learning for Advanced Manufacturing Automation: Balta, Lygeros, Rupenyan 

• Rethinking frequency control: Hug, Dörfler 

• A PAC-Bayes framework for optimal control: from individual policy design to lifelong learning control: Ferrari Trecate, Krause 

• Distributionally robust optimal control: Ferrari Trecate, Kuhn, Lygeros 

• Mean-field Multi-Agent Reinforcement learning (MF-MARL) for fair resource allocation: Frazzoli, He 

• Governance, regulations and control of on-demand services in complex transport networks: Geroliminis, Ferrari Trecate, Lygeros

• Definitions of fairness in Control: Hannak, Elger, Shaw

• Bilevel Optimization over Probability Space: Dörfler, He, Kiyavash

• Integrated planning and operation of energy systems: Heer, Lygeros

• RailWise: Smart Energy Strategies for Efficient Train Operations: Jones, Corman

• Optimal partitioning of energy communities: Hug, Kamgarpour

• Data-Driven Nonlinear Control of High Precision Robotic Systems: Karimi, Rupenyan

• Causal Hierarchical Reinforcement Learning (CHRL): Kiyavash, He, Krause

• Distributionally Robust Convex Reinforcement Learning: Krause, Kuhn

• Efficient Vertical Integration in Hierarchical Control of Manufacturing Systems: Lygeros, Balta, Rupenyan

• System Identification and Adaptaive Control for Interconnected Systems: Mastellone, Dörfler

• Control and Decision Making for Reliability and Lifetime Optimization: Mastellone, Censi, Frazzoli, (Zardini)

• Enhanced Ancillary Service Provision through Small-Scale Electric Networks: Ortmann, Hug, Medici

• Dynamic stochastic learning of train dynamics as enabler to highly automated train operation: Corman, Rupenyan

• Separation Principles in Online Learning and Control Dörfler, Zeilinger Scalable Gaussian Processes for Learning-Based MPC: Zeilinger, Jones

Please apply directly through the corresponding project leads. The NCCR Automation management team remain available for answering questions.