PhD project in Federated Machine Learning for Spectroscopy

torsdag 08 okt 20

Send ansøgning

Frist 29. oktober 2020
Du kan søge om jobbet ved DTU Compute ved at udfylde den efterfølgende ansøgningsformular.

Ansøg online

DTU Compute’s Section for Cognitive Systems, would like to invite applications for a 3-year PhD position starting early 2020. The project is funded by EU Horizon 2020 and is part of a multidisciplinary research project titled SERSing: “Advanced Surface-enhanced Raman Spectroscopy (SERS) based technologies for gas and liquids sensING in the area of chemical protection”.

The goal of the SERSing project is to enable fast detection and identification of chemical hazards at low concentrations in gas and liquid phase.

The aim of the PhD project is to develop federated machine learning that can be used to analyse spectroscopic data gathered by the project partners.

The PhD candidate will be part of the Section for Cognitive Systems, lead by Prof. Lars Kai Hansen. The section is a lively and research oriented group of scientists and support staff with a shared interest in information processing in man and computer, and a particular focus on the signals they exchange - audio, imagery, behavior – and the opportunities these signals offer for modeling and engineering of cognitive systems.

T
he Section is working actively to keep a healthy work-life balance and we are aware of the challenges facing young families in academia. Working at DTU provides much flexibility and families in Denmark enjoy a highly developed and affordable childcare system.

Project Description
The aim is to develop new and efficient statistical methods for analysing data from surface enhanced Raman spectroscopy (SERS). SERS is a powerful experimental technique that utilizes plasmonic nanostructures to enhance otherwise very weak Raman scattering signals. This tremendous amplification of the Raman scattering signal enables detection of trace amounts of target molecules. With recent advances in SERS instrumentation, it is possible to acquire large-area Raman maps at high speeds. Analysing these data requires new precise, fast, and reliable statistical machine learning methods. This project aims at advancing statistical spectral analysis, by replacing the current state-of-the-art pipeline of signal processing algorithms by a joint statistical model, optimized end-to-end using machine learning. The machine learning algorithms will be deployed to a cloud solution and should be able to be trained in a data-privacy preserving manner using federated machine learning.

Responsibilities and tasks
Are you interested in developing new statistical machine learning methods based on probabilistic modeling, deep learning, federated learning, and computer science? Then you might be our new PhD student. You will be involved in all tasks of the project. Your tasks will be

  • Develop and implement statistical machine learning methods for analyzing surface enhanced Raman spectroscopy data.
  • Develop, implement, and deploy the methods developed in a federated machine learning environment.
  • Contribute to documenting and disseminating the research results in scientific journals and conferences.
  • Develop, document, and publish open source software to make the research available to the community.
  • Maintain the dialogue with our industry and research partners to ensure that our research is aligned with their needs.

Through the project will gain a deep understanding of Raman spectroscopy and learn to master the latest statistical machine learning and deep learning methods in theory and practice. We expect that you are motivated and self-driven and strive for excellence. 

Qualifications
Candidates should have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. The master degree should be in computational science and engineering (CSE), applied mathematics, or engineering, or equivalent academic qualifications.

Preference will be given to candidates who can document experience in statistical machine learning. Experience in spectroscopy will be positively considered. Furthermore, good command of the English language is essential. 

Approval and Enrolment

The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in the DTU Compute PhD School Programme. For information about the general requirements for enrolment and the general planning of the PhD study programme, please see the DTU PhD Guide.

Assessment
The assessment of the applicants will be made by Associate Professor Tommy Sonne Alstrøm and Professor Lars Kai Hansen. 

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The position is a full-time position. The period of employment is 3 years starting 1 January 2020 (or as soon as possible thereafter).

You can read more about career paths at DTU here

Further Information
Further information concerning the project can be obtained from Associate Professor Tommy Sonne Alstrøm, tsal@dtu.dk

Further information concerning the application is available at the DTU Compute PhD homepage

Application
Please submit your online application no later than 29 October 2020 (local time)Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply online", fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

  • A letter motivating the application (cover letter)
  • Curriculum vitae
  • Grade transcripts and BSc/MSc diploma
  • Excel sheet with translation of grades to the Danish grading system (see guidelines and Excel spreadsheet here)

Candidates may apply prior to obtaining their master's degree, but cannot begin before having received it. Applications and enclosures received after the deadline will not be considered.

All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.

DTU Compute
DTU Compute is a unique and internationally recognized academic environment spanning the science disciplines mathematics, statistics, computer science, and engineering. We conduct research, teaching and innovation of high international standard - producing new knowledge and technology-based solutions to societal challenges. We have a long-term involvement in applied and interdisciplinary research, big data and data science, artificial intelligence (AI), internet of things (IoT), smart and secure societies, smart manufacturing, and life science.

Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear vision to develop and create value using science and engineering to benefit society. That vision lives on today. DTU has 12,000 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. Our main campus is in Kgs. Lyngby north of Copenhagen and we have campuses in Roskilde and Ballerup and in Sisimiut in Greenland.