PhD fellowship in Chemistry
PhD Project in Scattering data analysis using Machine Learning
Department of Chemistry
Faculty of SCIENCE
University of Copenhagen
Department of Chemistry invites applicants for a PhD fellowship in Project in Scattering data analysis using Machine Learning. The project is part of the research project AIChemy in collaboration with researchers from Department of Computer Science at University of Copenhagen.
Start date is expected to be 1 March 2022 or as soon as possible thereafter.
The student will be employed within the AIchemy project. The aim is to develop machine learning methods for analysis of synchrotron X-ray total scattering data for structural characterization of solid state materials. X-ray scattering is essential for characterization of the atomic structure of materials, but the data analysis required to go from data to structure is a bottleneck in materials discovery and development. There is a huge potential for significantly advancing data analysis methods in X-ray science through data science. The project is thus expected to broadly influence the way material characterization with scattering methods is done through close collaborations between groups in computer science and chemistry.
Methodologically, the PhD student will likely contribute to:
- Experimental collection of synchrotron data. We focus on in situ total scattering data from metal oxide nanoparticle formation, and the student will be heavily involved in developing and planning these experiments.
- Initial analysis of collected synchrotron X-ray data using conventional brute force method (for bench marking)
- Development of new Machine Learning methods for analysis of scattering data in collaboration with computer science researchers. We will focus on the use of deep learning methods. Tasks include developing and exploring latent variable models that capture a chemically meaningful embedding space, which can further be used in clustering and feature analysis of the data.
Who are we looking for?
The PhD project involves both experimental work (e.g.synchrotron experiments) and high-level data analysis. Experience in advanced X-ray scattering data analysis (or related fields) is an advantage, as is experience with synchrotron measurements. Experience and interest in data science techniques and coding is required.
Our group and research – and what do we offer?
The work will be done in the Nanostructure group, led by Kirsten Marie Ørnsbjerg Jensen. The group works in materials chemistry and focuses on applications of X-ray and neutron scattering for structure studies of nanomaterials. The student will also have a formalized collaboration with the Department of Computer Science at the University of Copenhagen, where another PhD scholarship is offered focusing on the development of Machine Learning methodologies. This setup offers world-class collaboration opportunities regarding the methodological as well as the inter-disciplinary aspects of the project.
The group is a part of Department of Chemistry at University of Copenhagen, Faculty of SCIENCE, University of Copenhagen. We are located in Copenhagen. We offer creative and stimulating working conditions in a dynamic and international research environment.
Principal supervisor is Associate Professor Kirsten M. Ø. Jensen, Department of Chemistry, email@example.com, Direct Phone: +45 35334797
Co-supervisors are Assistant Professor Raghavendra Selvan and Professor Erik B. Dam from the Department of Computer Science
The PhD programme
Depending of your level of education, you can undertake the PhD programme as either:
Option A: A three year full-time study within the framework of the regular PhD programme (5+3 scheme), if you already have an education equivalent to a relevant Danish master’s degree.
Option B: An up to five year full-time study programme within the framework of the integrated MSc and PhD programme (the 3+5 scheme), if you do not have an education equivalent to a relevant Danish master´s degree – but you have an education equivalent to a Danish bachelors´s degree.
Option A: Getting into a position on the regular PhD programme
Qualifications needed for the regular programme
To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. chemistry, physics or nanoscience or other relevant area. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.
Terms of employment in the regular programme
Employment as PhD fellow is full time and for maximum 3 years.
Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.
The terms of employment and salary are in accordance to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure.
Salary range starts at DKK 27,588.59 per month incl. PhD supplement (1 April 2021 level).
Option B: Getting into a position on the integrated MSc and PhD programme
Qualifications needed for the integrated MSc and PhD programme
If you do not have an education equivalent to a relevant Danish master´s degree, you might be qualified for the integrated MSc and PhD programme, if you have an education equivalent to a relevant Danish bachelor´s degree. Here you can find out, if that is relevant for you: General assessments for specific countries and Assessment database.
Terms of the integrated programme
To be eligible for the integrated scholarship, you are (or are eligible to be) enrolled at one of the faculty’s master programmes in chemistry, nanoscience or physics or other relevant area.
Students on the integrated programme will enroll as PhD students simultaneously with completing their enrollment in this MSc degree programme.
The duration of the integrated programme is up to five years, and depends on the amount of credits that you have passed on your MSc programme. For further information about the study programme, please see: www.science.ku.dk/phd, “Study Structures”.
Until the MSc degree is obtained, (when exactly two years of the full 3+5 programme remains), the grant will be paid partly in the form of 48 state education grant portions (in Danish: “SU-klip”) plus salary for work (teaching, supervision etc.) totalling a workload of at least 150 working hours per year.
A PhD grant portion is DKK 6,243.
Salary is DKK 221.34 per working hour (+ 12.5% holiday pay).(1 April 2021 level)
When you have obtained the MSc degree, you will transfer to the salary-earning part of the scholarship for a period of two years. At that point, the terms of employment and payment will be according to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure. Salary range starts at DKK 27,568.13 per month incl. PhD supplement.
Responsibilities and tasks in both PhD programmes
- Complete and pass the MSc education in accordance with the curriculum of the MSc programme
(ONLY when you are attending the integrated MSc and PhD programme)
- Carry through an independent research project under supervision
- Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE
- Participate in active research environments, including a stay at another research institution, preferably abroad
- Teaching and knowledge dissemination activities
- Write scientific papers aimed at high-impact journals
- Write and defend a PhD thesis on the basis of your project
We are looking for the following qualifications:
- Professional qualifications relevant to the PhD project
- Relevant publications
- Relevant work experience
- Other relevant professional activities
- Curious mind-set with a strong interest in materials characterization, coding and method development.
- Good language skills
Application and Assessment Procedure
Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.
- Motivated letter of application (max. one page)
- Curriculum vitae including information about your education, experience, language skills and other skills relevant for the position
- Original diplomas for Bachelor of Science or Master of Science and transcript of records in the original language, including an authorized English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted.
- Publication list (if possible)
- Reference letters (if available)
The deadline for applications is 17 October 2021 (year), 23:59 GMT +2.
We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.
The further process
After the deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified, whether you will be passed for assessment.
The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process here.
For specific information about the PhD fellowship, please contact the principal supervisor.
General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website: https://www.science.ku.dk/phd/
The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position.