Sort by
Refine Your Search
-
Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling Job No.: 683222 Location: Clayton campus Employment Type: Full-time Duration: 3.5 to 4-year fixed
-
phenomena across scales, combining multiple fields including physics, mathematics, astronomy, history & philosophy of science, and social science. Its approach to societal engagement throughout the project’s
-
mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
-
Position Description The Unsteady Flow Diagnostics Laboratory (UNFoLD) led by Prof. Karen Mulleners at EPFL in Lausanne is looking for multiple PhD students to join the group in the fall of 2025 or early
-
will inform future race strategy and live race tactics. Multiple factors influence the strategy and tactics in professional road cycling, and these have changed significantly since the COVID-19 pandemic
-
project (Decarbonization of Heating and Cooling), we are seeking a motivated and qualified PhD candidate to design integrated district heating and cooling systems. Future thermal networks based on renewable
-
sources such as (i) atmospheric models, (ii) satellite remote sensing, (iii) land use information, and (iv) meteorological data. The aim of this PhD is to develop and implement models for integrating data
-
to accommodate their own particular input setup and deciding the best modelling practice. This PhD project will aim at automatic solution development, supporting flexible input setups and addressing in one
-
vulnerable parts of the system for attacks, model faults and attack risks, and develop new control architectures that mitigates them towards achieving operational resilience throughout their life. Your PhD
-
are seeking a highly motivated PhD candidate to develop efficient on-device generative AI systems based on large language models (LLMs). The project focuses on creating compact, low-latency, and energy