With more than 1,000 employees, including 135 pioneering principal investigators, as well as a population of about 3,400 passionate students, the Faculty of Applied Sciences is an inspiring scientific ecosystem. Focusing on key enabling technologies, such as quantum- and nanotechnology, photonics, biotechnology, synthetic biology and materials for energy storage and conversion, our faculty aims to provide solutions to important problems of the 21st century. To that end, we train students in broad Bachelor's and specialist Master's programmes with a strong research component. Our scientists conduct ground-breaking fundamental and applied research in the fields of Life and Health Science & Technology, Nanoscience, Chemical Engineering, Radiation Science & Technology, and Engineering Physics. We are also training the next generation of high school teachers and science communicators.
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Department of Bionanoscience
The Department of Bionanoscience is dedicated to pioneering, fundamental research at the interface between physics and biology. This young, international department was established in 2010 and has since expanded to currently 15 faculty members. The department offers excellent general facilities, including an advanced optical microscopy facility, a cryo-EM facility, extensive general cell biology and biochemistry facilities, large high-performance computing clusters and a large cleanroom and nanofabrication facility, the Kavli Nanolab. The department strives for a collegial, nourishing, and inclusive environment.
Artificial intelligence (AI) concepts are propelling nearly all computer vision-intensive applications in life science, biomedical research, space exploration, high-tech manufacturing, and security technology. While traditional image processing methods are based on linear space-invariant assumptions, neural networks are inherently non-linear and have the potential to outperform these methods. Neural networks are trained to perform a certain task using very large sets of data. The feature of adapting to data by extracting the essential information and using it to form decisions or make predictions in a “black box” is what makes this approach so useful for many applications. For scientific applications, however, this black box causes a serious dilemma: what is gained in performance is lost in interpretability of the solution. Also lost is the ability to integrate existing physical knowledge of the system. The aim of the IRIS lab is to open the black box of AI and develop methodologies for context-independent, knowledge-based learning of imaging systems that will address fundamental challenges in all quantitative imaging applications. The proposed AI-technology will be applied to electron, optical, and ultrasound imaging to unravel dynamic molecular processes in living organisms: conformational ensembles of proteins, single-molecule dynamics in thick tissue and super-resolved vasculature mapping in real-time. In this project you will work within IRIS to tackle one of the frontiers of cryo-EM imaging by developing methods to extract the continuous heterogeneity of molecular conformations from cryo-EM data.
IRIS Lab is a Delft Artificial Intelligence Lab. Artificial intelligence, data and digitalisation are becoming increasingly important when looking for answers to major scientific and societal challenges. In a DAI-lab, experts in ‘the fundamentals of AI technology’ along with experts in ‘AI challenges’ run a shared lab. As a PhD, you will work with at least two academic members of staff and three other PhD candidates. In total, TU Delft will establish 24 DAI-Labs where 48 Tenure Trackers and 96 PhD candidates will have the opportunity to push the boundaries of science by using AI. Each team is driven by research questions which arise from scientific and societal challenges and contribute to the development and execution of domain specific education. Instead of the usual 4-year contract, you will receive a 5-year contract. Approximately a fifth of your time will be allocated to developing ground breaking learning materials and educating students in these new subjects. The experience you will gain by teaching will be invaluable for future career prospects. All team members have many opportunities for self-development. You will be a member of the thriving DAI-Lab community that fosters cross-fertilization between talents with different expertise and disciplines.
TU Delft offers DAI-Lab PhD-candidates a 5-year contract (as opposed to the normal 4-years), with an official go/no go progress assessment after one year. Approximately a fifth of your time will be allocated to developing ground breaking learning materials and educating students in these new subjects.
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2395 per month in the first year to € 3217 in the fifth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills. The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.
For information about this vacancy, you can contact Arjen Jakobi, Assistant Professor, email: A.Jakobi@tudelft.nl.
For information about the selection procedure, please contact Ms. Irina Bruckner, HR advisor, email: application-3mE@tudelft.nl.
Interested applicants should send their detailed curriculum vitae, the names of two professional referees, a list of courses taken with grades obtained in their BSc and MSc program, a list of publications (if any), a summary of their MSc thesis, and a cover letter stating their motivation before August 1st to: application-3mE@tudelft.nl. When applying for this position, please refer to vacancy number TUD00307.
A pre-employment screening can be part of the application procedure.
Acquisitie naar aanleiding van deze vacature wordt niet op prijs gesteld.