Postdoc Position: Absorption Spectroscopy

This 2-year postdoctoral project aims at building a new generation of FTSs able to cover the entire spectral bandwidth of the particular supercontinuum source developed by the company Norblis (a spin-off from the Technical University of Denmark) while using cost-effective components. This will be part of the world's first automated multi-species trace gas analyser acting as an interactive storage monitor and alert system in fruit storage facilities. In this project, you will design and build a robust and compact FTS, coordinate your research with that of the other partners in the project, and contribute to the dissemination of results.

Postdoc Position: Absorption Spectroscopy

This 2-year postdoctoral project aims at building a new generation of FTSs able to cover the entire spectral bandwidth of the particular supercontinuum source developed by the company Norblis (a spin-off from the Technical University of Denmark) while using cost-effective components. This will be part of the world's first automated multi-species trace gas analyser acting as an interactive storage monitor and alert system in fruit storage facilities. In this project, you will design and build a robust and compact FTS, coordinate your research with that of the other partners in the project, and contribute to the dissemination of results.

PhD Position: Closed-Loop Optimization of Experimental Parameters for Neurotechnological Systems

Neurotechnological systems such as brain-computer interfaces (BCIs) allow us to record and interpret the brain activity of healthy users, patients or animal models in real time. Thus, BCIs not only allow us to study fundamental brain functions but they also provide applications for communication, for the control of devices, or to support the treatment of neurological or psychiatric diseases. As brain signals are individual, noisy and high dimensional, machine learning methods play a crucial role in extracting information about the ongoing brain state.

Parameters of an experimental protocol can strongly influence the measured brain signals, but parameters that are suitable for one participant may not be for another. This calls for individually optimised protocol parameters. Ideally, individual best parameters are determined in a closed-loop approach during a single experimental session. As the measured EEG / MEG / LFP / sEEG / ECoG signals are very noisy, either only a small number of parameter sets can be evaluated within one session, or each parameter set needs to be rated based on a very small amount of brain signals which, of course, may deliver noisy ratings.

The PhD project investigates optimisation approaches for parameters of neurotechnological applications with the goal to cope with noisy objective functions. The focus will be on how (1) experimental protocol parameters and (2) machine learning methods for the decoding of brain signals can be co-optimised. For both tasks, domain-specific regularisation approaches shall be explored.

As a PhD candidate, you will investigate novel optimisation strategies in simulations before translating them into experiments with a human participant in the loop. You will be expected to design and implement experimental protocols in Python. You will conduct non-invasive and invasive closed-loop experiments in our own EEG labs, in labs of our DBI2 partners or clinics, and train machine learning models to analyse our own data and the data of our scientific partners. You will help disseminate the results in high-impact papers and scientific journals, and at conferences and workshops.

This is a fixed-term (4 year), full-time position. You will be expected to participate in teaching activities involving Bachelor’s and Master’s degree students, which will take 10% of your working time. Throughout the project, you will receive guidance from Dr Michael Tangermann and be an integral part of the Data-Driven Neurotechnology Lab. The lab is situated within the Machine Learning and Neural Computing department and embedded in the Donders Institute.

Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate.

PhD Position: Closed-Loop Optimization of Experimental Parameters for Neurotechnological Systems

Neurotechnological systems such as brain-computer interfaces (BCIs) allow us to record and interpret the brain activity of healthy users, patients or animal models in real time. Thus, BCIs not only allow us to study fundamental brain functions but they also provide applications for communication, for the control of devices, or to support the treatment of neurological or psychiatric diseases. As brain signals are individual, noisy and high dimensional, machine learning methods play a crucial role in extracting information about the ongoing brain state.

Parameters of an experimental protocol can strongly influence the measured brain signals, but parameters that are suitable for one participant may not be for another. This calls for individually optimised protocol parameters. Ideally, individual best parameters are determined in a closed-loop approach during a single experimental session. As the measured EEG / MEG / LFP / sEEG / ECoG signals are very noisy, either only a small number of parameter sets can be evaluated within one session, or each parameter set needs to be rated based on a very small amount of brain signals which, of course, may deliver noisy ratings.

The PhD project investigates optimisation approaches for parameters of neurotechnological applications with the goal to cope with noisy objective functions. The focus will be on how (1) experimental protocol parameters and (2) machine learning methods for the decoding of brain signals can be co-optimised. For both tasks, domain-specific regularisation approaches shall be explored.

As a PhD candidate, you will investigate novel optimisation strategies in simulations before translating them into experiments with a human participant in the loop. You will be expected to design and implement experimental protocols in Python. You will conduct non-invasive and invasive closed-loop experiments in our own EEG labs, in labs of our DBI2 partners or clinics, and train machine learning models to analyse our own data and the data of our scientific partners. You will help disseminate the results in high-impact papers and scientific journals, and at conferences and workshops.

This is a fixed-term (4 year), full-time position. You will be expected to participate in teaching activities involving Bachelor’s and Master’s degree students, which will take 10% of your working time. Throughout the project, you will receive guidance from Dr Michael Tangermann and be an integral part of the Data-Driven Neurotechnology Lab. The lab is situated within the Machine Learning and Neural Computing department and embedded in the Donders Institute.

Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate.

Assistent-controller FNWI

Als assistent-controller ondersteun je de financial- en projectcontroller binnen de afdeling F&C door zorg te dragen voor het opstellen van financiële rapporten, begroting en prognoses. Deze functie omvat het uitvoeren van financiële analyses, het afstemmen met de financiële administratie en het waarborgen van de naleving van externe en interne financiële regelgeving door middel van periodieke controles en analyses.

Daarnaast bied je ondersteuning bij audits en draag je in overleg van het (project) management zorg voor de project control van eenvoudige subsidieprojecten. Ook verleen je ondersteuning aan de voorbereiding van de meer complexe subsidieverantwoordingen en projectbegrotingen. Je draagt zorg voor planning en control producten voor de stafafdelingen en je doet dit in nauwe samenwerking met de faculteitscontroller. Je hebt een brugfunctie tussen control en financiële administratie.