Over 20 Fully Funded PhD scholarships at Technical University of Denmark/Study in Denmark
- Omran Aburayya
- May 8, 2024
- 1 min read
Updated: May 14, 2024
If you dream of securing a PhD scholarship to study in Denmark, you're in the right place, Technical University of Denmark (DTU) is inviting aspirants pursuing PhD. degree to fill over 20 vacant fully funded PhD. positions in various fields. See below and click on any position to visit the official application portal.
A motivated, curious, and outgoing PhD candidate with a solid background in optical communication, laser physics or nonlinear optics. Experience with machine learning, digital signal processing and experimental work is a plus.
The project goal is to explore various machine learning techniques to realize feedback control that can enable spectral shaping and quantum noise limited operation. The project will cover both numerical simulations, algorithm development as well as experimental implementations.
Application Deadline: 31/05/2024
Your objective will be to create the bioinformatic tools that enables isoform-level analysis of proteomics data as well as apply them within proteomics.
Requirements: the following requirements must be met by the successful candidate:
Strong R programming skills
Experience with applied analysis of proteomics or transcriptomics (bulk/single-cell)
Any of the following features are highly desirable, but not strictly required:
Experience with developing bioinformatic tools (webserver/package)
Experience with machine learning
Strong background in molecular/cellular biology
Experience with analyzing large and complex datasets
Application Deadline: 1 June 2024 (23:59 Danish time)
The PhD project is at the intersection of data science, optimization and control, all with applications to wind energy science. You will be a part of an international team developing open-source tools capable of supporting the development of multi-disciplinary and multi-objective optimized wind farm control functions for grid compliance and services. If you have the skills but not the experience in wind energy science, we would still like to hear from you. The same is valid if you feel you only have part of the skills, but the desire and ability to improve on the rest.
We are looking for a self-motivated and team-oriented person who thrives in a collaborative environment and enjoys working with complex, multi-disciplinary topics.
A good candidate will fulfil most of the following points:
Knowledge in one or more of the following fields:
Data driven methods ideally with application to wind energy science
Data-driven wind farm layout optimization
Data-driven active power control in large wind power plants
Experience in programming with scientific Python
Application deadline: 31 May 2024 (23:59 Danish time).
In this project you will learn and apply experimental methods and techniques to study and characterize sound and vibration on the millimetre and sub-millimetre scale as well as statistical methods to evaluate metrological aspects such as reproducibility, repeatability, and uncertainty. You will:
Handle precision measurement equipment such as laser vibrometers, miniature accelerometers, actuators, measurement microphones, and impedance tubes;
Process measurement data with statistical methods;
Study and evaluate the influence of different uncertainties on overall measurement reliability;
Establish benchmark cases and optimize measurement procedures.
The project is closely linked to ongoing and upcoming PhD projects on computational modelling of uncertainty and statistical analysis methods. The outcome of the project will be the creation of new and improved measurement procedures and data processing, allowing deeper understanding of the underlying coupled physics, more informed representation in corresponding computer models, and better adapted designs.
Your required qualifications and skills are:
A master level education in the fields of either acoustics, mechanics, measurement technology, physics, or equivalent;
Excellent command of English both spoken and written;
Experience in setting up lab experiments and measurement procedures
Knowledge in statistics, uncertainty analysis, or metrology
You must 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.
16 June 2024 (23:59 Danish time)
DTU Chemistry at the Technical University of Denmark is seeking highly motivated candidates for a PhD scholarship in the field of catalysis to develop new sustainable routes to produce selected organic commodity solvents. The PhD scholarship is a three-year position starting in September 2024, or shortly hereafter, and the project is done in collaboration with an international industrial partner.
Our research focuses on catalysis and chemistry for the sustainable production of fuels and chemicals. The aim of the PhD project is to develop high-performing and durable supported metal-based catalyst systems for the synthesis of selected organic solvents with industrial importance from renewable resources.
Your tasks in the project will include syntheses of catalysts and testing of their performance in both batch and continuous-flow mode. Furthermore, you characterize the materials using various ex-situ techniques and draft scientific publications as well as interim project reports.
What we’re looking for:
We are looking for a bright and motivated PhD student who is driven by curiosity, has a strong experimental mindset, and enjoys collaborating with both industrial and international research partners. The ideal candidate will bring the following to the project:
A MSc degree in chemistry, chemical engineering, or other relevant fields
Enthusiasm and a let’s go solve attitude
Creativity, persistence, and attention to detail
Excellent communication skills in English (both spoken and written)
Documented scientific achievements and experience with catalysis, instrumental analytical methods, materials chemistry and characterization, or flow synthesis will be considered an advantage but is not a prerequisite for applying.
You must 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.
Application deadline: 26 May 2024 (23:59 Danish time).
The PhD project is expected to break new ground at the absolute forefront of enzyme bioinformatics and discovery research. Apart from being part of a unique International and Interdisciplinary research training program, the PhD project is at the core of DTU’s efforts of pioneering the use of advanced digitalization methods to map, discover, and predict the function of new enzymes to provide new fundamental knowledge and pave the way for new innovative applications.
Experience with genome and protein sequence analysis, bioinformatics, and Python programming, preferably within enzyme biotechnology is required. Practical experience with carbohydrate processing enzymes and/or SQL is a major advantage.
You must be curious, passionate, and have excellent communication skills in English both verbally and in writing and be able and happy to travel to take part in the common MSCA GLYCO-N activities.
As a PhD student at DTU Bioengineering you will work in a highly supportive and collaborative team that in addition to the supervisor includes postdocs and young faculty members with advanced skills and experience in bioinformatics, molecular biology, and analyses for carbohydrate active enzymes characterization.
You must 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, and for this PhD position you must not have worked in Denmark before.
Application deadline: 31 May 2024 (23:59 Danish time).
Responsibilities and qualifications
You will work on a project to design fluorinated polymers prepared through light-induced polymerization. The design space for finding the optimal composition and synthesis conditions for polymer electrolytes is too vast to explore manually. Therefore, we are combining self-driving labs, high-throughput atomic scale simulations, and AI to transition to digitalization-based automation of electrolyte development. The immediate goal is to create and utilize digital twins to develop high-performing polymer electrolytes for Mg-ion batteries (i.e., high ionic conductivity, coulombic efficiency, and cyclability at a low cost). However, we expect the gained know-how and infrastructure to be transferred to other battery chemistries and power-to-X applications.
Your role as a PhD student will be to design and build the autonomous experimental setup using off-the-shelf electronics and custom parts. The project has many parts, including building/expanding our hardware setup, writing control and analysis software using Python programming language, and utilizing high-throughput computational methods, such as density functional theory (DFT) calculations. In addition to being an integral part of a collaborative team, you will also work closely with a collaborating group at the University of Queensland, Australia, and spend a minimum of three months visiting our partner.
Qualified candidates must have:
Good working knowledge of electrochemistry.
Ability to work independently and plan and carry out complicated tasks.
Good communication skills in English, both written and spoken.
A proactive approach to problem-solving, capable of identifying new directions and opportunities within the project.
In addition, it is desirable to have:
Experience with building custom experimental hardware using, e.g., Arduino.
Experience in Python programming and git repository.
Experience in computational materials science, e.g., DFT simulations.
You must 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.
2 June 2024 (23:59 Danish time).
Responsibilities and qualifications:
Your overall focus will be to strengthen the department’s competences within the field of optical computing and optical neural networks. You will work closely with colleagues, and with our collaborators both academic and industrial partners in Denmark as well as abroad.Your primary tasks will include:
Carry out research on the integrated digital optical neural networks.
Design and develop relevant silicon photonics integrated circuit, tape out for foundry and packaging services.
Conduct system demonstrations in real-world neural network applications such as natural language processing tasks with our collaborators in the University of Copenhagen.
Disseminating your research findings in highly ranked peer-reviewed journals and international conference proceedings.
Strengthening our international collaboration with academic and industrial partners through your external stay.
Following courses on topics related with your project, either at DTU or other institution.
Contributing to teaching and supervision tasks at DTU.
Your required qualifications and skills are:
Excellent English communication skills both spoken and written.
A master level education in the fields of silicon photonics, optical computing, or optical signal processing.
As an ideal candidate, you should also have:
A solid background in optical computing, or optical neural networks.
Experience in simulation, design, and characterization of silicon photonics devices and circuits. Prior experience in a cleanroom environment is a plus.
A comprehensive understanding of microelectronics and working experience with FPGAs, high-speed transceivers, and PCB design.
The ability to work independently while effectively collaborating in projects with both academic and industrial partners.
Experience in research dissemination, either publications or presentations.
You must 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.
Application deadline: 31 May 2024 (23:59 Danish time)
Project description
One of the central challenges facing modern machine learning is to understand and quantify uncertainty to ensure that AI-driven solutions deliver accurate and trustworthy insights. In this project, our goal is to develop novel methods for uncertainty quantification in deep neural networks. In particular, we will focus on graph neural networks applied to the problem of molecular discovery. We envision that your role could be focused on developing and scaling techniques such stochastic Markov chain Monte Carlo (MCMC) sampling.
Responsibilities
During the PhD program, you are expected to:
Develop novel methods for uncertainty quantification in deep learning.
Work with state-of-the-art nerual network architectures applied to molecular data.
Publish scientific papers and present research results in top machine learning conferences such as NeurIPS, ICML, UAI, and AISTATS.
Assist in machine learning teaching and supervision.
Qualifications
Candidates should have the following required skills:
Proven experience in Bayesian methods, probabilistic modeling, and probability theory.
A strong grasp of the theoretical foundations and practical implementation of Markov chain Monte Carlo (MCMC) methods.
Proven experience with implementing machine learning methods in Python and Pytorch/Tensorflow.
High level of motivation and creative problem solving skills.
Excellent communication and writing skills in English.
You must 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.
Application deadline: 12 May 2024 (23:59 Danish time).
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