Anders Andersson

The Division of Robotics, Perception and Learning at KTH Royal Institute of Technology invites applications for a doctoral student position focused on image representations for class discovery. This WASP-funded project aims to advance generative approaches for out-of-distribution discovery and novel species identification, with a particular emphasis on fine-grained classification tasks such as plankton species identification. The research will integrate genomic measurements into the identification process, developing robust algorithms that are application area independent but with environmental and biological relevance. The successful candidate will join a collaborative environment involving Associate Professor Josephine Sullivan (RPL), Professor Anders Andersson (Environmental Genomics, SciLife Lab), and Bengt Karlson (SMHI). The PhD student will also be part of the WASP graduate school, benefiting from interdisciplinary expertise and scientific output, as highlighted in the article "The ocean's smallest creature is mapped." The project offers opportunities to contribute to cutting-edge research in computer vision, deep generative learning, and environmental genomics. Applicants must meet the eligibility requirements for postgraduate education as outlined by the Swedish Higher Education Ordinance. This includes holding a second cycle degree (such as a master's) or equivalent, or completing at least 240 higher education credits with 60 at the second-cycle level. Practical proficiency in deep learning programming libraries (TensorFlow, PyTorch, JAX) is mandatory, and experience with GPU-based experimentation and cluster computing (Docker, Slurm) is advantageous. English proficiency equivalent to English B/6 is required. Selection criteria include academic achievements, completed courses, demonstrated programming ability, and personal skills such as independence, collaboration, professionalism, and analytical thinking. Specialization in computer vision and/or machine learning is highly desirable. The position is full-time, temporary, and offers a monthly salary according to KTH's doctoral student salary agreement. Employment is for up to four years, with renewal options, and includes employee benefits and a supportive workplace. The doctoral student may perform limited additional tasks (up to 20%) related to training and administration. The position is based in Stockholm, Sweden, and may be subject to security clearance if classified as security-sensitive. To apply, candidates must submit a complete application through KTH's recruitment system, including certified copies of diplomas, grades, proof of language requirements, CV, and relevant publications or technical reports. Applications must be received by midnight CET on the closing date. For further information, contact Associate Professor Josephine Sullivan at [email protected]. KTH Royal Institute of Technology is a leading international technical university committed to education, research, and innovation for a sustainable society. The university values equality, diversity, and equal opportunities, offering a creative and dynamic environment for personal and professional growth.

The Division of Robotics, Perception and Learning at KTH Royal Institute of Technology invites applications for a doctoral student position focused on image representations for class discovery. This WASP-funded project aims to advance generative approaches for out-of-distribution discovery and novel species identification, with a particular emphasis on fine-grained classification tasks such as plankton species identification. The research will integrate genomic measurements into the identification process, developing robust algorithms that are application area independent but tested in environmental and biological contexts. The successful candidate will join a collaborative environment, working with Associate Professor Josephine Sullivan (RPL), Professor Anders Andersson (Environmental Genomics, SciLife Lab), and Bengt Karlson (SMHI). The PhD student will also be part of the WASP graduate school, benefiting from interdisciplinary expertise and access to cutting-edge resources. The project is designed to contribute to scientific advances in both computer vision and environmental genomics, as highlighted in recent collaborative publications. Applicants must meet the eligibility requirements for postgraduate education as outlined by the Swedish Higher Education Ordinance. This includes holding a second cycle degree (master's or equivalent), or completing at least 240 higher education credits with 60 at the second-cycle level, or possessing equivalent knowledge. Practical proficiency in deep learning programming libraries such as TensorFlow, PyTorch, or JAX is mandatory, and experience with GPU-based experimentation and cluster computing (e.g., Docker, Slurm) is advantageous. English proficiency equivalent to English B/6 is required. Selection criteria include academic results, completed courses, demonstrated programming ability, and personal skills, with a strong preference for candidates specialized in computer vision and/or machine learning. The position offers full-time employment for up to four years, with renewal options, and includes a monthly salary according to KTH's doctoral student salary agreement. Doctoral students are entitled to a workplace with employee benefits and may perform certain tasks within their role, such as training and administration, up to 20% of their time. The research environment at KTH is creative and dynamic, with a commitment to equality, diversity, and sustainability. To apply, candidates must submit their application through KTH's recruitment system, including copies of diplomas, grades, certificates of fulfilled language requirements, CV, and representative publications or technical reports. Certified translations are required if documents are not in English or Swedish. Applications must be received by midnight CET on the closing date. For further information, contact Associate Professor Josephine Sullivan at [email protected]. Join KTH Royal Institute of Technology and contribute to shaping the future of education, research, and innovation in a leading international technical university.