Job vacancies

The NCCR Automation is looking for talented scientists in the area of digitalization, automation and control. Join us!

25 PhD and Postdoc positions starting in 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 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. 

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