{"id":2562570,"url":"https://alion.io/job/tulane-postdoctoral-fellow-25","title":"Postdoctoral Fellow","company":{"id":320153,"name":"Tulane","domain":"tulane.edu","url":"https://alion.io/company/tulane","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":78,"open_postings":9,"ghost_share":0,"stale_share":0.889,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-15T05:46:00Z"}},"role":"Science & Research","role_family":"Science & Research","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New Orleans, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":77000,"max_usd":203000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":542},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Fortran","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-09-09T15:13:19Z","employer_posted_date":"2026-09-09","last_verified_at":"2026-10-15T16:26:53Z","board_verified":true,"closed_at":null,"days_open":36,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":36},"description":"The Physics and Engineering Physics Department at Tulane University and the Quantum and Condensed Matter Physics Group in the Theoretical Division of Los Alamos National Laboratory (LANL) have an immediate opening of a postdoctoral position in the area of computational materials physics. The postdoc will focus on applying first-principles density functional/time-dependent density functional theory (DFT, TDDFT) and high-throughput approaches in combination with machine learning techniques for discovery and inverse design of novel 2D d-electron materials, including those with non-trivial topological properties. The work will be a combination of development and implementation of TDDFT models for magnonic excitations, development of computational workflows, and applying existing DFT codes. Successful applicants will join a highly collaborative project aimed at unraveling the fundamental properties of these systems, establishing a new route to quantum information, and laying the foundation for future quantum information devices, and will be an integral part of a dynamic team of condensed matter theory and experimental scientists at Tulane and LANL. The position is renewable for up to four years. The postdoc will be based at Tulane and will make two two-week visits per year to LANL.\nWhat You Need\nMinimum Job Requirements:\nA successful candidate must demonstrate strong skills in condensed matter theory/computational materials theory with an excellent scientific record of publications. Expertise in first-principles density functional theory simulations and expertise with TDDFT or other many-body theory is required, while a knowledge of machine learning is highly desired, but not necessary. The ability to work creatively as a part of a diverse team and independently is important. Strong written and oral communication skills are essential.\nEducation: Candidates must have a Ph.D. in Physics, Chemistry, or Materials Science (preferably completed within the last 5 years).\nDesired Qualifications:\nProgramming skills in either Python, C++, or Fortran.\nExperience working in Linux computing environments, especially in the context of high-performance computing.\nWorking experience with standard DFT/TDDFT methods, i.e. VASP, Yambo.\nNote to Applicants:\nAs part of your application, please submit a resume, publication list, research interest, a brief statement of future plans, and the names of three references.\nFurther inquiries can be addressed to Prof. Jianwei Sun (), Prof. Adrienn Ruzsinszky (), Prof. John P. Perdew (), or Dr. Chris Lane ().\nWhere You Will Work\nTulane is a private research university located in the uptown residential area of culturally unique New Orleans. Los Alamos National Laboratory (LANL), in beautiful northern New Mexico, is a multidisciplinary research institution engaged in strategic science on behalf of national security.\nAccess to LANL\nDue to federal restrictions contained in the current National Defense Authorization Act, citizens of the People's Republic of China-including the special administrative regions of Hong Kong and Macau-as well as citizens of the Islamic Republic of Iran, the Democratic People's Republic of Korea (North Korea), and the Russian Federation, who are not Lawful Permanent Residents (\"green card\" holders) are prohibited from accessing facilities that support the mission, functions, and operations of national security laboratories and nuclear weapons production facilities, which includes Los Alamos National Laboratory.","description_format":"text","description_chars":3574,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Higher Education"],"lifecycle":[{"event":"open","at":"2026-10-14T21:12:11Z"}],"visa":[],"liveness":{"score":19,"band":"cold","label":"Long shot","p_open":1,"p_active":0.351,"p_room":0.55,"age_days":35,"expected_fill_days":24,"reasons":["conf:0","evergreen","win:tail"],"computed_at":"2026-10-15T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/tulane-postdoctoral-fellow-25","json_url":"https://alion.io/job/tulane-postdoctoral-fellow-25.json","meta":{"generated_at":"2026-10-15T19:19:16Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":29161,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-16T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":320153},"rest":"https://alion.io/mcp/rest/get_company?id=320153"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ftulane-postdoctoral-fellow-25"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Ftulane-postdoctoral-fellow-25"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Ftulane-postdoctoral-fellow-25"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/tulane-postdoctoral-fellow-25\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ftulane-postdoctoral-fellow-25"}]}