جستجوی استاد راهنمای دانشگاهی

Professor Stefan Goedecker is a distinguished faculty member in the Department of Physics at the University of Basel, Faculty of Science. He holds the position of Professor of Computational Physics and leads an active research group focused on developing advanced computational methods for materials science and quantum physics. Dr. Goedecker received his physics education at the Technical University Munich and the College of William and Mary, followed by a Ph.D. from EPFL Lausanne. His postdoctoral training included positions at Cornell University and the Max-Planck Institute in Stuttgart. In 2003, he was appointed Professor of Computational Physics at the University of Basel, where he has established himself as a leading researcher in computational methods development. His research interests center on computational physics with emphasis on electronic structure calculations, atomistic simulations, and the development of novel algorithms for materials science applications. His work has strong interdisciplinary connections spanning physics, mathematics, material sciences, chemistry, and computer science. Current research directions include machine learning applications in catalysis, fourth-generation neural network potentials for molecular chemistry, and methods for quantifying material synthesizability. Analysis of his recent publications reveals a strong focus on advancing computational methods for electronic structure calculations, with particular emphasis on machine learning potentials, molecular dynamics optimization, and accurate modeling of material properties. His work bridges theoretical physics with practical applications in materials science and nanotechnology, with increasing integration of artificial intelligence techniques into traditional computational physics frameworks. Machine learning for Catalysis (Ongoing) Fourth-Generation Neural Network Potentials for Molecular Chemistry (Completed) Towards Quantifying the Synthesizability of Materials (Completed) Professor Goedecker's research group operates within the Department of Physics at the University of Basel, which is part of the NCCR SPIN initiative focused on silicon-based quantum computing development. The department hosts over 20 research groups with more than 180 teaching staff members, creating a vibrant research environment for computational physics and quantum technologies.

• Computational Physics
• Electronic Structure Calculations
• Atomistic Simulations
• +۴ مورد دیگر

Michael Herbst is an Assistant Professor (tenure-track) at EPFL, holding a joint appointment in the School of Basic Sciences (SB) and the School of Engineering (STI). He leads the Mathematics for Materials Modelling (MatMat) research group, focusing on error control in atomistic simulations, density-functional theory (DFT), and interdisciplinary computational methods. His work bridges mathematics, materials science, and computer science, emphasizing robust algorithms and Julia-based software development. Herbst holds a PhD from Heidelberg University and has held postdoctoral positions at RWTH Aachen and Inria Paris. He is a core member of the MARVEL and CESMIX research centers. Education: 2018: Dr. rer. nat. (magna cum laude), Heidelberg University 2009–2013: BA and MSci (1st class) in Natural Sciences, University of Cambridge 2008–2009: Studies in Mathematics/Physics, TU Kaiserslautern Research Interests : Herbst's research centers on developing reliable computational methods for materials modeling, including error estimation in DFT, black-box SCF algorithms, and Julia-based tools like the Density-Functional Toolkit (DFTK). His work addresses challenges in high-throughput simulations, numerical stability, and interdisciplinary collaboration across mathematics, physics, and computer science. Grants & Projects : MARVEL Center for Computational Design (EPFL) CESMIX Center for Extreme-Scale Simulations (MIT) EMC² Project (Sorbonne/Inria/École des Ponts) Awards : HGS MathComp PostDoc Fellowship (2018–2021) DAAD Travel Funding (2018) Exploratory Research Space Fund (RWTH Aachen, 2022) Labs & Teams : Head of the MatMat group at EPFL, focusing on error-controlled simulations and open-source software development.

• Materials Science
• Computational Chemistry
• Density Functional Theory
• +۳ مورد دیگر

Andrea Cavalli is an Adjunct Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with both the VPA-AVP-CP CECAM-GE unit and the School of Basic Sciences (SB) within the ISIC department (SB-CECAM). His research focuses on advanced biosensor technologies, implantable medical devices, and personalized medicine applications. He teaches a course on drug discovery, emphasizing computational methods for drug design and pharmacokinetics. Key research interests include nanotechnology-driven biosensors, biocompatible materials for implantable devices, and multiplexed detection systems for infectious diseases. Cavalli leads projects on in vivo validation of biochips for metabolic monitoring and integrates electrochemical sensors with circuit design for real-time diagnostics. His work spans from fundamental material science to clinical translation, with a focus on HIV/tuberculosis diagnostics and chronic disease management. PhD Student: Alessia Ghidini Lab: CECAM (European Center for Atomistic and Molecular Simulations) Key collaborations: Molecular diagnostics platforms, nanomaterials for bioelectronics Publications highlight innovations in nanoplasmonic biosensors, biocompatible packaging solutions, and CNT-based bioelectronics. Ongoing work explores remote patient monitoring systems and multipanel biosensors for personalized therapy optimization.

• Drug Discovery
• Biosensors
• Implantable Medical Devices
• +۳ مورد دیگر

Jigyasa Nigam is a Research Fellow in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), School of Engineering, holding the prestigious MIT-IBM Distinguished Postdoctoral Fellowship and previously funded by the School of Engineering’s Postdoc Fellowship for Excellence in Engineering. Her academic journey includes a PhD in Computational Science and Modeling from École Polytechnique Fédérale de Lausanne (EPFL), completed in August 2024, and a Master’s degree from the Indian Institute of Space Science and Technology. Dr. Nigam’s research centers on developing machine learning frameworks that rigorously incorporate physical constraints—such as symmetry (equivariance), smoothness, and conservation laws—into atomic-scale simulations. Her work bridges quantum chemistry and artificial intelligence, focusing on electronic structure modeling, Hamiltonian learning, and multi-scale approaches for molecular systems. Key innovations include advancing completeness theory for atomic representations and unifying message-passing schemes with equivariant features. Analysis of her publication trends reveals a cohesive trajectory toward physically grounded machine learning: early work established mathematical foundations for N-body representations (2020-2022), while recent publications (2024-2025) demonstrate scalable applications in protein dynamics, quantum chemistry, and coarse-grained modeling, emphasizing generative AI and differentiable frameworks. Scientific recognition includes: School of Engineering’s Postdoc Fellowship for Excellence in Engineering MIT-IBM Distinguished Postdoctoral Fellowship As an early-career researcher, Dr. Nigam is actively advancing her fellowship projects without formal advisees. Her work is supported exclusively by her named fellowships, targeting symmetry-breaking phenomena and open quantum systems for applications in energy storage and pharmaceuticals. She collaborates within MIT’s EECS ecosystem and maintains ties to EPFL’s Lab of Computational Science and Modeling, where she conducted her doctoral research under Michele Ceriotti’s supervision.

• Machine Learning
• Atomistic Simulations
• Quantum Chemistry
• +۳ مورد دیگر

Dr. Jiang Cao is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich. His research focuses on quantum transport phenomena in nanoscale devices, with an emphasis on ab initio simulations of electronic and optoelectronic systems. Key areas include nanoelectronics, thermoelectric materials, and 2D material-based devices. He specializes in advanced computational methods such as the GW approximation and non-equilibrium Green's functions for modeling quantum transport and device performance. Dr. Cao’s work spans topics like spin-charge qubits in van der Waals heterostructures, electron-electron interactions in nanodevices, and exascale simulations for nanoelectronics. His contributions include developing the Jiezi open-source Python framework for quantum transport simulations. His research addresses challenges in next-generation transistors, photovoltaic effects, and ultrafast material characterization. His recent articles highlight advancements in strained FinFET quantum devices, bulk photovoltaic effects in centrosymmetric materials, and the role of carrier-carrier interactions in sub-threshold transistor behavior. These studies bridge fundamental physics with practical device engineering, targeting applications in high-performance electronics and energy-efficient technologies.

• Quantum Transport
• Nanoelectronics
• Thermoelectric Materials
• +۳ مورد دیگر

Prof. Dennis M. Kochmann is a Full Professor and Head of the Department of Mechanical and Process Engineering at ETH Zurich. His research focuses on theoretical, computational, and experimental solid mechanics, particularly linking microstructure to macroscopic material properties. He leads the Mechanics & Materials Lab, exploring architected metamaterials, instabilities across scales, and advanced modeling techniques like phase field and atomistic-to-continuum coupling. His work spans materials such as metals, ceramics, and active composites, with applications in additive manufacturing and energy absorption. Kochmann holds a Dr.-Ing. from Ruhr-University Bochum and has held academic roles at Caltech before joining ETH in 2017. His awards include the NSF CAREER Award, ERC Consolidator Grant, and multiple society recognitions. He serves on editorial boards and contributes to academic governance roles at ETH and international societies.

• Solid Mechanics
• Materials Science
• Metamaterials Design
• +۵ مورد دیگر

Prof. Dennis Kochmann is a Full Professor of Mechanics and Materials and Head of the Department of Mechanical and Process Engineering at ETH Zurich. His research focuses on theoretical, computational, and experimental solid mechanics, particularly the link between microstructure and macroscopic material properties. Key interests include metamaterials, architected materials, and instabilities across scales. His group develops advanced computational techniques like phase-field modeling and atomistic-to-continuum coupling. Education: He holds a Dipl.-Ing. from Ruhr-University Bochum (2005), a Master's from UW-Madison (2006), and a Dr.-Ing. from Ruhr-University Bochum (2009). He has held academic roles at Caltech before joining ETH Zurich in 2017. Research emphasizes architected materials with controllable properties, such as nano/micro-truss networks and active composites. Applications span additive manufacturing, energy absorption, and nonlinear dynamics. His work bridges mechanics, materials science, and computational methods. Awards: NSF CAREER Award, ERC Consolidator Grant, Bureau Prize (IUTAM), Richard von Mises Prize (GAMM) Leadership: Head of Department (since 2023), former Head of Institute of Mechanical Systems (2018-2020) Editorial Roles: Associate Editor for Applied Mechanics Reviews , Archive of Applied Mechanics , and others Labs/Teams: Leads the Mechanics & Materials Lab, focusing on computational and experimental material studies. Collaborates on projects involving AI-driven materials design and metamaterials optimization.

• Solid Mechanics
• Materials Science
• Metamaterials
• +۴ مورد دیگر

Jean-François Molinari is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), directing the Computational Solid Mechanics Laboratory (LSMS) and holding joint appointments in the Civil Engineering Institute and Materials Science Institute. He has worked internationally at Johns Hopkins University, Ecole Normale Supérieure Cachan, and Ecole Polytechnique de Paris. His research spans damage mechanics , multiscale modeling , tribology , fracture dynamics , and contact mechanics , integrating atomistic to macroscopic scales. Recent work includes Physics-informed neural networks for friction Dynamic fragmentation in granular media Exascale molecular dynamics with adaptive clustering as seen in his 2024-2025 publications. He has supervised numerous PhD students in projects related to wear modeling , crack propagation , and multiscale geomechanics . His group develops open-source tools like Tamaas and Akantu , and he contributes to SNSF Research Council and editorial roles.

• Computational Solid Mechanics
• Multiscale Modeling
• Tribology
• +۴ مورد دیگر

Ehsan Rahmatizad Khajehpasha is a Research Fellow at the University of Basel's Department of Physics, part of the Computational Physics Group led by Prof. Stefan Goedecker. He holds a PhD in Computational Physics from the Institute for Advanced Studies in Basic Sciences (Zanjan, 2016–2021), a Master's in Solid-state Physics from the University of Zanjan (2014–2016), and a Bachelor's in Solid-state Physics from the Iran University of Science and Technology (2010–2014). His research focuses on advancing computational methods for atomistic simulations, including machine learning potentials, charge equilibration techniques, and structure prediction algorithms. Key projects include the development of the FLAME library for atomistic modeling environments and contributions to the Minima Hopping algorithm for materials discovery. Recent work emphasizes entropic contributions in phase transitions and improving the accuracy of density functional theory calculations using surface integral approaches. Publications highlight advancements in computational physics, such as optimizing geometry calculations, accelerating machine learning potentials, and analyzing free energy landscapes in molecular crystals. Collaborations span interdisciplinary topics like bioinformatics (e.g., predicting BRCA gene variants) and agricultural genomics (vernalization gene prediction). He is affiliated with the Philosophisch-Naturwissenschaftliche Fakultät and actively contributes to the Computational Physics Group's projects in materials science and quantum chemistry.

• Computational Physics
• Materials Science
• Machine Learning
• +۳ مورد دیگر

Dr. Xingfan Zhang is a postdoctoral researcher at the University of Basel's Department of Physics within the Computational Physics Group led by Prof. Stefan Goedecker, where he investigates metal/oxide-supported catalytic systems for CO/CO 2 conversion using machine-learned potentials and electronic-structure calculations. He joined the group in March 2025 after completing his PhD at University College London under Prof. Sir Richard Catlow. His academic background includes: 2025–present: Postdoc, University of Basel, Switzerland 2020–2024: PhD in Chemistry, University College London, UK 2017–2020: Master in Materials Science, Shandong University, China 2013–2017: Bachelor in Materials Science and Engineering, Shandong University, China A computational scientist specializing in DFT, molecular dynamics, and Monte Carlo simulations, Zhang's research centers on metal oxide materials—particularly ceria—with emphasis on electronic structures, defect chemistry, and surface reactivity for catalytic applications. His methodology integrates interatomic potentials, hybrid QM/MM models, and minima hopping algorithms to bridge theoretical modeling and practical energy conversion challenges. His publication trends (2019–2025) reveal a concentrated focus on ceria-based systems, addressing defect heterogeneity, dopant effects, and band-edge properties. This work demonstrates escalating sophistication in computational materials science, with recent studies leveraging machine learning to resolve surface reactivity and defect clustering phenomena critical for catalytic design. As part of the Computational Physics Group, Zhang contributes to interdisciplinary projects spanning physics, chemistry, and computer science. The group develops atomistic simulation methods for structure prediction and materials discovery, with current priorities on noise-tolerant force calculations and entropic stabilization in complex molecular crystals—aligning with Zhang's expertise in catalytic system modeling.

• Computational Physics
• Materials Science
• Chemistry
• +۵ مورد دیگر

Joseph William Abbott serves as a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL) within the Laboratory of Computational Science and Modelling (COSMO) under the Institute of Materials in the School of Engineering. He joined EPFL in October 2022 and is concurrently enrolled in the Doctoral Program in Chemistry and Chemical Engineering. Abbott earned his MSci Master's degree in Chemistry from the University of Bristol (2016-2020), including a study-abroad year at the University of Washington where he researched noble gas-doped nanodiamonds under Xiaosong Li. His undergraduate work also included a summer position at STFC neutron facilities near Oxford developing scientific software for neutron scattering data analysis, followed by a Master's thesis on quantum dynamics of photosynthetic energy transfer with Fred Manby at Bristol. His research focuses on machine learning applications for electron density modeling using physics-inspired molecular representations, spanning quantum chemistry, electronic structure theory, molecular dynamics, and battery simulation. Abbott emphasizes open-source scientific software development, with industrial experience from a year-long algorithm development role at BIOVIA (Dassault Systèmes) in Cambridge focused on solid electrolyte interphase growth modeling for lithium-ion batteries. As part of COSMO laboratory operations, Abbott contributes to computational science infrastructure while advancing his doctoral work at the intersection of quantum mechanics and machine learning. His technical profile includes active presence on GitHub, ORCID, and Google Scholar, with research outputs centered on atomistic modeling methodologies.

• Machine Learning
• Quantum Chemistry
• Electronic Structure Theory
• +۴ مورد دیگر

Alessia Ghidini is a Doctoral Assistant at the SB-European Center for Atomistic and Molecular Simulations (SB-CECAM), part of the Institute of Chemical Sciences and Engineering (ISIC) within the School of Basic Sciences at École polytechnique fédérale de Lausanne (EPFL). She is concurrently enrolled as a doctoral student in the Doctoral Program in Chemistry and Chemical Engineering (EDCH) under the EDOC school at EPFL. Her research is centered on atomistic and molecular simulations , with applications in chemical sciences and materials engineering. Her work involves computational modeling to understand molecular-level phenomena in complex chemical systems. As a researcher at EPFL, she contributes to advancing simulation methodologies within the SB-CECAM center, which promotes computational science in Europe. While no publications or advising roles are currently listed, her position indicates active engagement in high-level scientific research. Scientific Awards: No awards listed. Advising and Grants: No formal students or grant details are mentioned. As a Doctoral Assistant, she is likely supported by institutional or project-based funding related to computational chemistry at EPFL. Labs and Teams: She is affiliated with the SB-CECAM (European Center for Atomistic and Molecular Simulations), a research node promoting collaborative computational science at EPFL.

• Computational Chemistry
• Molecular Simulations
• Atomistic Simulations
• +۳ مورد دیگر

Vladislav Slama is a Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with the School of Basic Sciences and the Institute of Chemical Sciences and Engineering (ISIC). He works within the Laboratory of Computational Chemistry and Biochemistry (LCBC), focusing on computational methods for modeling biological pigment-protein complexes, molecular dynamics simulations, and quantum mechanical calculations of molecular properties. His research utilizes tools like AMBER, Gaussian, PyScf, and Quantarhei for simulating excitation dynamics and optical spectra. Key areas include atomistic modeling, environmental effects on molecular systems, and quantum master equation approaches.

• Computational Chemistry
• Biochemistry
• Molecular Dynamics
• +۳ مورد دیگر

Michele Ceriotti is an Associate Professor at the École polytechnique fédérale de Lausanne (EPFL) , affiliated with the School of Engineering (STI) and the Institute of Materials (IMX) . He directs the Computational Science and Modeling Laboratory (COSMO) , focusing on atomistic modeling, statistical mechanics, and machine learning. His roles include teaching in the EPFL Doctoral Programs and serving on editorial boards for Journal of Chemical Physics and Physical Review Materials . PhD in Physics, ETH Zürich Junior Research Fellow (JRF), Merton College, Oxford His research centers on developing machine learning models for quantum chemistry, molecular dynamics, and materials science, with a focus on open-source software like i-PI and chemiscope . Recent work includes integrating symmetry into atomic-scale learning and advancing uncertainty quantification in neural networks. Recent publications span topics such as electronic density of states , high-entropy alloy surface segregation , and data-driven chemistry , highlighting his expertise in machine learning and quantum systems . He has received the Junior Research Fellow at Merton College, Oxford award and is actively involved in mentoring PhD students and postgraduates. His laboratory, COSMO, collaborates on multi-energy systems and contributes to the atomistic modeling community through software development and editorial leadership.

• Atomistic computer simulations
• Statistical mechanics
• Machine learning
• +۴ مورد دیگر

Peter Michael Derlet is a senior staff scientist at the Paul Scherrer Institut (PSI) in Switzerland and serves as an Adjunct Professor in the Department of Materials at ETH Zurich. He also holds a position as Visiting Scientist at the Department of Materials Engineering, Bundesanstalt für Materialforschung und -prüfung (BAM) in Berlin, Germany. His research spans multiple areas of condensed matter physics and materials science, with a focus on computational modeling of complex material systems. Derlet's research interests encompass the mechanical properties of materials with complex defect structures, dynamical properties of local and extended defects, development of simulation tools for experimental measurements using neutrons, photons and electrons, classical magnetization dynamics in micro- and meso-scopic magnetic systems, vibrational and configurational dynamics of structural glasses, avalanche statistics using simplified dislocation dynamics, and potential energy landscape exploration algorithms. His work bridges theoretical modeling with experimental validation across diverse material systems. Analysis of his recent publications reveals a strong focus on metallic glasses, magnetic systems, and radiation-damaged materials. His research shows increasing integration of computational modeling with experimental techniques like neutron scattering, with particular attention to topological defects in spin ice systems, structural dynamics in amorphous materials, and magnetic phase transitions. The interdisciplinary nature of his work connects condensed matter physics, materials science, and computational methods. Gilbert Hausmann prize (2021) for Hugo Bocquet's Masters Thesis Derlet actively supervises students at multiple levels, including PhD candidates Hugo Bocquet (2021-), Dominik Schildknecht (2016-2019), Soumya Swayamjyoti (2012-2016), and Araceli Valles Sales (2010-2013), as well as Masters and Bachelor students. He collaborates on SNF-funded PhD projects in Physics/Materials science on 'Magnetism in Correlated Disorder' with Tom Fennell at PSI. His teaching includes Thermodynamics and Phase Transformations and Computational multi-scale modeling of solids at ETHZ. Derlet's work with the Condensed Matter Theory group at PSI involves developing advanced simulation tools to model complex material behaviors, with particular emphasis on connecting microscopic mechanisms to macroscopic properties in disordered and frustrated systems.

• Mechanical properties of materials with complex defect structures
• Dynamical properties of local and extended defects
• Simulation tools for experimental measurement of structural disorder
• +۵ مورد دیگر