Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
be developed and implemented in the GEOS-Chem chemical transport model, coupled to the Community Earth System Model. Standardized large wildfire events will be simulated based on historical data and
-
transfer and optimization of waste heat harvesting. Responsibilities and qualifications Your role will be to simulate the heat transfer processes between façade panels and thermoelectric generators, focusing
-
The ideal candidate will have one or more of the following: A strong research background in optical computing or optical neural networks. Experience in simulation, design, and characterization of silicon
-
complete deduction methods for establishing properties of quantum programs - Reduction methods and metrics for quantum systems - Decision diagrams for efficient analysis and simulation of distributed
-
letter of application (max. one page). CV incl. education, work/research experience, grant/awards, language skills and contact details of 2 academic references. A certified/signed copy of a) PhD
-
. The candidate is hence expected to have most of the following qualifications: Expert knowledge in the control of robots Experience in simulation of robotic systems Experience in design and development of robotic
-
, modelling and machine learning to improve defect detection, classification and power loss simulations. Benchmarking field-acquired images with laboratory measurements. Publishing results in leading journals
-
Denmark (Odense Campus). The Postdoctoral Fellow is expected to carry out numerical simulations, nanofabrication and experimental characterization of optical metasurfaces with embedded quantum emitters
-
on the intrinsic stochasticity and non-volatility of nano-devices. Implementing simulation frameworks and experimental validation through lab characterization of devices and circuits. Designing and evaluating energy
-
computationally efficient at simulating permanent magnet structures, and capable of leading to the globally optimal solution of the underlying design optimization problem. We aim to combine gradient-based