Melbourne Polytechnic

Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers of tomorrow and creating novel solutions to major global challenges. Our community is made up of 120 nationalities, 14 000 students, 400 professors and close to 5000 faculty and staff working on our dynamic campus in Espoo, Greater Helsinki, Finland. Diversity is part of who we are, and we actively work to ensure our community’s diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community. The School of Electrical Engineering is one of the six schools of Aalto University. Our portfolio covers fields from natural sciences to engineering and information sciences. In parallel with basic research, we develop ideas and technologies further into innovations and services. We are experts in systems science; we develop integrated solutions from care of the elderly to space robotics. We are now looking for a Doctoral Researcher in quantum computing and algorithms. Are you as excited about quantum technology and its future applications as we are? We are now looking for a doctoral researcher to join the Micro and Quantum Systems (MQS) research group in developing quantum computing schemes for practical applications at Aalto University’s Electronics and Nanoengineering department. In this position you will have a chance to take a deep dive into quantum computing and make a real impact by high-quality research. Join us in shaping the future! Your role and goals Your primary task as a doctoral researcher is to conduct research into quantum algorithms and information, and thus earn your PhD degree. You are expected to take an active role in advancing the research projects of the MQS group. Our recent research has focused on the theory and applications of variational quantum algorithms and quantum machine learning. We also have activity in quantum optics, so additional experience and interest in this topic is considered a definite advantage. Theoretical research into the capabilities of quantum computing and quantum advantage is also a prospective topic, especially in relation to cryptography. The exact research topics can be adjusted within the limits set by our current projects according to the interests of the applicant. Your goal in research will be to produce world-class results, in collaboration with the other group members, and publish them in high-impact journals. In addition to research, you will also be expected to take courses as a part of your PhD degree and participate in the teaching activities of the research group. Your network and team  Currently, our research group consists of 1 professor, 1 lecturer, 1 staff scientist, 1 postdoc and 5 PhD students. The group is led by Professor Ilkka Tittonen. Your experience and ambitions  Applicants are expected to hold a Master’s degree and excellent study records in theoretical physics, mathematics, computer science, or other relevant field. have a keen interest in quantum computing have a working proficiency in English. (Finnish language is not required.) The research topics can be adjusted according to the background of the applicant. Familiarity with cryptography and/or quantum optics is an advantage. Teaching experience will also be a valuable asset. What we offer We offer the applicant the opportunity to conduct research in a vibrant and active national and international community. Finland hosts many active research groups in quantum computing connected via a national quantum consortium called InstituteQ with many quantum-related activities. We also have access to real quantum hardware, VTT’s Q50 machine, which sits right downstairs from our offices. Aalto University also offers ample opportunities for multidisciplinary research and teaching collaboration. We offer you interesting work in an inspiring work environment. You will work in a community of students, researchers and other professionals, where we promote socially significant goals in the fields of science and education. We will familiarize you with your tasks and you will become part of a friendly and competent team that will offer you support in your work tasks also in the future. We work in a hybrid way, and the primary workplace is Otaniemi, Espoo. The Otaniemi campus is a thriving and connected community of 100 nationalities, 13,000 students and 4,500 employees. Life at the transformed campus is vibrant and filled with amazing architecture, calming nature, and a variety of cafes, restaurants, services and good connections along the recently opened metro line. Join us! To apply, please share your application with us through our recruitment site ("Apply now!”) at the latest by March 31, 2026. Applications submitted by email will not be accepted. We encourage you to apply early, as we will go through applications, and we may invite suitable candidates to interview already during the application period. Transcript of study records CV Motivation letter Recommendation letters (max. 2, if available) Please note: Aalto University’s employees should apply for the position via our internal HR system Workday (Internal Jobs) by using their existing Workday user account (not via the external webpage for open positions). If you are a student or visitor at Aalto University, please apply with your personal email address (not aalto.fi) via Aalto University open positions. For more information regarding the open position, please contact university lecturer Matti Raasakka, matti.raasakka@aalto.fi or professor Ilkka Tittonen, ilkka.tittonen@aalto.fi. In any question regarding the recruitment process, please contact HR Advisor, hr-elec@aalto.fi. Want to know more about us and your future colleagues? You can watch these videos: Aalto University – Towards a better world, Aalto People, and Shaping a Sustainable Future. Read more about working at Aalto: https://www.aalto.fi/en/careers-at-aalto Check out our new virtual campus experience: https://virtualtour.aalto.fi/ About Finland  Finland is a great place for living with or without family – it is a safe, politically stable and well-organized Nordic society. Finland is consistently ranked high in quality of life and was just listed again as the happiest country in the world. For more information about living in Finland: https://www.aalto.fi/en/careers-at-aalto/for-international-staff. More about Aalto University: Aalto.fi youtube.com/user/aaltouniversity linkedin.com/school/aalto-university/ www.facebook.com/aaltouniversity instagram.com/aaltouniversity To view information about Workday Accessibility, please click here. Please see more of our Open Positions here.

About Mohammed VI Polytechnic University (UM6P): Located at the heart of the Green City of Benguerir, Mohammed VI Polytechnic University (UM6P), a higher education institution with an international standard, was established to serve Morocco and the African continent and to advance applied research and innovation. This unique university, with state-of-the-art infrastructure, has woven an extensive academic and research network, and its recruitment process is seeking outstanding academics and professionals to promote Morocco and Africa’s innovation ecosystem. About the department Vanguard works on the development of innovative and interdisciplinary applied research projects. From technological innovation to the transfer of research to industry, Vanguard has also the mission of developing an ecosystem of related start-ups. For more information about our Center, please visit our webpage: https://vanguard.um6p.ma/ Offer description: There are many systems of interest to scientists that are composed of individual parts or components linked together in some way. Examples include the Internet, a collection of computers linked by data connections, human societies, which are collections of people linked by acquaintance or social interaction, transportation systems and biological interactions. These systems are represented as networks. A network is a set of objects that are connected to each other in some fashion. Mathematically, a network is represented by a graph, which is a collection of nodes that are connected to each other by edges. The nodes represent the objects of the network and the edges represent relationships between objects. A common way to represent a graph is to use the adjacency matrix associated with the graph. However, adjacency matrices only model networks with one kind of objects or relations between the objects. Many real world networks have a multidimensional nature such as networks that contain multiple connections. For instance, transport networks in a country when considering different means of transportation. The train and bus routes are different types of connections and should in some models be represented by different kinds of edges. These kind of situations can be modeled using multilayer networks which emphasize the different kind or levels, known as layers, of connections between the elements of the network and the interactions between these levels as well. In order to capture the structure and complexity of relationships between the nodes of networks with a mul-tidimensional nature, tensors are used to represent these kind of networks. For example, the transport network mentioned earlier would be represented by a 4th order tensor A 2RN_L_N_L  where L is the number of the layers (transportation means) and N is the number of nodes (stations or stops). Using convenient tensor products, the goal is to define measures to analyze different multidimensional networks based on their adjacency tensors. However, collecting all the interactions in the systems and sometimes even observing all the components is a challenging task. In most cases, only a sample of a network is observed. Therefore, network completion needs to be addressed. Matrix completion methods have proved to be efficient when reconstructing a non fully observed data. These methods can be applied to complete or predict links in a network. However, missing information in a network can include both missing edges and nodes which makes classical matrix completion method insufficient. However,we may collect other information and features about the elements of the network. Therefore, side information about the nodes along with the observed edges need to be exploited. The problem of network completion arrises also for applications where the network has a multidimensional representation such as multiplexes and multilayer networks. Since multidimensional networks can be represented by tensors, one can think of applying tensor completion methods which have proved to be efficient in many applications such as image and video reconstruction. However, the same issue arises, tensor completion methods can not be directly applied to recover the links of the network giving the fact that the data is sparse most of the time. We aim to use auxiliary information about the multiplex and multilayer networks alongside with the observed links in order to predict or reconstruct the missing links. The first step is to explore different optimization methods using low rank tensor minimization and tensor decompositions paired with auxiliary information in order to recover missing links in a multilayer network with connected components. An important constraint in network completion is that the factorization must only capture the non zero entries of the tensor. The remaining entries are treated as missing values, not actual zeros as is often the case in sparse tensor and matrix operations. Therefore, the next step in this project is to address sparse optimization for tensors. We propose the integration of randomized algorithms into sparse optimization frameworks for the purpose of completing multidimensional networks by studying the theoretical foundations behind randomized algorithms in the context of sparse optimization and applications in real world data sets. We are also interested in exploring opportunities for  parallelism of the completion process, highlighting the potential for significant speedup in computations. Job responsibilities Research and Development: Conduct research to develop novel algorithms and methodologies for tensor completion in multidimensional networks. This includes exploring optimization techniques, tensor decompositions, and incorporating auxiliary information for more accurate completion. Algorithm Design: Design and implement algorithms for tensor completion, considering the unique challenges posed by sparse and multidimensional network data. This involves developing efficient and scalable algorithms that can handle large-scale datasets. Tensor Analysis: Analyze the structure and properties of multidimensional networks represented as tensors. Investigate different measures and metrics for characterizing network connectivity and relationships. Sparse Optimization: Address the challenge of sparse optimization for tensors by integrating randomized algorithms into optimization frameworks. Study the theoretical foundations of randomized algorithms in the context of sparse tensor operations and apply them to real-world datasets. Parallel Computing: Explore opportunities for parallelism in the tensor completion process to enhance computational efficiency. Investigate parallel algorithms and architectures that can exploit the inherent parallelism in tensor operations. Collaboration: Collaborate with interdisciplinary teams including computer scientists, statisticians, and domain experts to apply tensor completion techniques to real-world applications, especially in the case of social sciences. This involves effective communication and coordination to ensure the successful integration of mathematical methods into practical systems. Publication and Dissemination: Publish research findings in top-tier journals and present results at conferences and workshops. Disseminate knowledge and contribute to the academic community by sharing insights and methodologies developed during the course of the project. Mentorship and Training: Provide mentorship and guidance to graduate students and junior researchers involved in related projects. Share expertise and knowledge in applied mathematics, tensor analysis, and network science to foster the professional development of team members. Qualifications and experience essential PhD in Applied Mathematics in the fields of Numerical Linear Algebra, or equivalent. Prior experience on the subject is highly desired.

About Mohammed VI Polytechnic University (UM6P) Mohammed VI Polytechnic University (UM6P) is an internationally oriented institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development of Morocco and Africa. UM6P is an institution oriented towards applied research and innovation. On a specific focus on Africa, UM6P aims to position these fields as the forefront and become a university of international standing. More than just a traditional academic institution, UM6P is a platform for experimentation and a pool of opportunities, for students, professors, and staff. It offers a high-quality living and study environment thanks to its state-of-the-art infrastructure. With an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. In its research approach, the UM6P promotes transdisciplinary, entrepreneurship spirit and collaboration with external institutions for developing up to date science and at continent level in order to address real challenges. All our programs run as start-ups and can be self-organized when they reach a critical mass. Thus, academic liberty is promoted as far as funding is developed by research teams. The research programs are integrated from long-term research to short-term applications in linkage with incubation and start-up ecosystems. About the Chemical & Biochemical Sciences Green Process Engineering (CBS) The Chemical & Biochemical Sciences Green Process Engineering Department (CBS) is a component of the Mohammed VI Polytechnic University (UM6P). The main objective of CBS is to set up a distinctive research-teaching program, of international level, to meet the research and teaching challenges of UM6P, on green and environmental chemistry applied to all aspects of chemical sciences: organic and inorganic chemistry, analytical chemistry, chemical biology, biochemical and thermal reactions. Research at CBS is organized around several major areas, which aim to answer challenging industrial questions, from complex chemical and biochemical reactions to scale-up and validation of process engineering. CBS projects aim at an in-depth understanding of the molecular mechanisms of all transformations to propose new original alternatives in terms of efficiency, environmental friendliness, and sustainability. Job Description The CBS department at UM6P is seeking a highly motivated and qualified researcher for a Postdoctoral Researcher position in Inorganic Chemistry. The successful candidate will contribute significantly to advancing the department's research initiatives and supporting mission of research excellence and innovation. Key Responsibilities: Design and conduct research projects in inorganic chemistry, focusing on areas such as in inorganic chemistry, materials synthesis, or catalysis.​ Synthesize and characterize inorganic compounds or materials using techniques such as X-ray diffraction, NMR spectroscopy, and electron microscopy.​ Analyse experimental data, interpret results, and contribute to the development of new hypotheses and research directions.​ Prepare manuscripts for publication in peer-reviewed scientific journals and present research findings at national and international conferences.​ Collaborate with interdisciplinary research teams within the department and with external partners to enhance research outcomes.​ Candidate Criteria PhD degree in Chemistry, with a specialization in inorganic chemistry or a closely related area, is required. Research Excellence: A strong track record of high-quality research demonstrated through publications in reputable international journals. Collaborative Mindset: Proven ability to collaborate effectively with interdisciplinary teams and external partners to address complex challenges. Communication Skills: Excellent communication and presentation skills in English. Proficiency in French a plus. Entrepreneurial Spirit: Alignment with UM6P's focus on entrepreneurship. Application and Selection: The application folder must contain: Detailed CV, Cover letter along with a comprehensive presentation of the candidate background, research work, projects, and key activities (publications and achievements) Research and teaching statement, entrepreneurial ideas, and concepts if any, and services to UM6P community: max 3-4 pages, 3 reference letters. Our Offer Very competitive salary and benefits package. A unique set of research partners and collaborators.