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Ohio Wesleyan University

Delaware, United States
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About Ohio Wesleyan University

Basic information and contact details for Ohio Wesleyan University

Ohio Wesleyan University, founded in 1842, is a private liberal arts college located in the small city of Delaware, Ohio. Originally established by Methodists as a non-sectarian institution, PWU has seen significant renovation and expansion in recent years.

The main university campus is located in downtown Delaware, and effectively divided into two sections by one of the city’s main roads. The residential section offers a wide range of accommodation and dining options for the vast majority of students, indeed students are asked to live on campus for the full four years of their undergraduate degree course. Seven residential halls exist on campus, as well as a number of different dining facilities spread across the area, with a range of different meal plan options available. This is intended to foster a sense of “school spirit” amongst students of OWU, and to draw together the university’s students as a community.

The university’s athletics teams are known as the “Battling Bishops”, and compete in 21 different sports including baseball, American football, basketball, golf, lacrosse, soccer, swimming and tennis. Competing in NCAA Division III, athletics teams constitute one of the main sources of entertainment available to students of campus.

In terms of academic offerings, Ohio Wesleyan University has over 80 undergraduate majors of minors available, covering a wide variety of different academic fields of study.

Throughout its history, OWU has produced a number of famous alumni, including Nobel Prize-winning chemist Frank Sherwood Roland, as well as Avengers and Agents of S.H.I.E.L.D actor Clarke Gregg.

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We are looking for a Research Engineer to join our MOE AcRF Tier 1 funded research project at the Singapore Institute of Technology. You will be part of an interdisciplinary team spanning accounting and NLP/AI, working to design, build, and validate a pipeline that detects inconsistencies and contradictions between firms' written financial filings (10-K/10-Q) and spoken earnings call transcripts. The role involves implementing retrieval-augmented natural language inference models and LLMs, processing large-scale financial text data, and supporting empirical analysis of contradiction scores.  Job Responsibilities Drive the technical execution of the research project under the guidance of the PI and Co-PI, ensuring project deliverables and timelines are met. Design and implement the retrieval-augmented NLI pipeline for inconsistency and contradiction detection across corporate disclosures (earnings call transcripts and SEC filings). Perform large-scale text preprocessing, cleaning, segmentation, and indexing of earnings call transcripts and financial filing corpora. Implement claims extraction modules using LLM-based approaches to identify key assertions from corporate disclosures. Build semantic retrieval mechanisms (e.g., Sentence-BERT embedding similarity search) for matching claim-context pairs across disclosure channels. Implement and evaluate contradiction detection models using NLI frameworks and LLM prompting techniques. Aggregate model outputs into firm-quarter level scores and prepare structured datasets for empirical analysis. Support human-in-the-loop validation by coordinating with student annotators and preparing annotation guidelines. Maintain reproducible research workflows using version control (Git) and systematic documentation of processes, code, and findings. Job Requirements Bachelor’s degree or Master’s degree in Computer Science, Information Systems, Data Science, Artificial Intelligence, or a closely related field. Strong programming skills in Python, with experience in relevant libraries such as HuggingFace Transformers, PyTorch/TensorFlow, and scikit-learn. Prior experience or coursework in natural language processing, machine learning, or information retrieval. Familiarity with version control tools (e.g., Git/GitHub) and collaborative development practices. Experience with financial data or financial text analysis will be advantageous. Familiarity with LLM APIs, prompt engineering, and retrieval-augmented generation (RAG) workflows will be advantageous. Good communication skills and ability to work both independently and collaboratively.

Salary

Competitive

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14 May 2026

MOE START Scheme (Early Career Awards) - Business, Communication and Design

SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)

Singapore Institute of Technology (SIT)

Singapore

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Singapore Institute of Technology (SIT)

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At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical expertise and transferable skills to tackle tomorrow’s challenges. SIT collaborates with industry in our education, while benefitting them with our talent supply and collaborative research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic learning experiences within real-world environments. Through strategic partnerships forged by our faculty with industry, learners bridge theoretical knowledge with practical application in and out of the classroom. Such active & authentic learning opportunities ensures graduates step into their careers with applicable skills and competence. In research, SIT faculty aims to bridge knowledge creation and utilisation. Our faculty collaborates closely with industry on translational research and innovation with focus on addressing pertinent industry challenges to impact businesses.  The Singapore Teaching and Academic Research Talent (START) Scheme is co-funded by the Ministry of Education (MOE) and SIT to encourage and support outstanding young Singaporeans interested in pursuing an academic career at SIT. The Early Career Awards (ECA) are designed to support Singaporeans who have recently completed their postdoctoral training and prepare them for an academic career at SIT. This award aims to enhance the teaching capabilities of the recipient and help build a strong research portfolio. Eligibility and Selection Criteria Singapore Citizen Recently completed postdoctoral training Excellent academic record plus a high degree of proficiency and motivation for a research area that is strongly aligned with SIT’s supported areas  Strong passion for teaching, research and an academic career Benefits Awardees may receive research grants to help build a competitive research portfolio. Upon completion of the award, awardees will be considered for Assistant Professorship. Awardees will also be assigned a faculty mentor for the duration of the scheme. Application Process Applications are open throughout the year. Applications received before 15 January 2026 will be considered for intake 2026.  Interview by the respective clusters for shortlisted applicants will be conducted throughout the year.  Final Interview by Panel for shortlisted applicants will take place from March to April 2026. Awards for intake 2026 will be finalised by July 2026. Supported Areas for START Business, Communication and Design Financial and Sustainability Accounting: The measurement, reporting, and analysis of an organisation's financial performance alongside its environmental, social, and governance (ESG) and carbon footprint. Integrating traditional financial accounting principles with sustainability considerations to provide stakeholders with a more comprehensive view of a company's long-term value creation. Meetings, Incentives, Conferences and Exhibitions (MICE): The planning, management, and execution of business events, corporate gatherings, and large-scale conferences. This field is a key segment of the tourism and hospitality industry, requiring expertise in event management, business tourism, marketing, logistics, and stakeholder engagement.

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We are looking for a Research Engineer to join our MOE AcRF Tier 1 funded research project at the Singapore Institute of Technology. You will be part of an interdisciplinary team spanning accounting and NLP/AI, working to design, build, and validate a pipeline that detects inconsistencies and contradictions between firms' written financial filings (10-K/10-Q) and spoken earnings call transcripts. The role involves implementing retrieval-augmented natural language inference models and LLMs, processing large-scale financial text data, and supporting empirical analysis of contradiction scores.  Job Responsibilities Drive the technical execution of the research project under the guidance of the PI and Co-PI, ensuring project deliverables and timelines are met. Design and implement the retrieval-augmented NLI pipeline for inconsistency and contradiction detection across corporate disclosures (earnings call transcripts and SEC filings). Perform large-scale text preprocessing, cleaning, segmentation, and indexing of earnings call transcripts and financial filing corpora. Implement claims extraction modules using LLM-based approaches to identify key assertions from corporate disclosures. Build semantic retrieval mechanisms (e.g., Sentence-BERT embedding similarity search) for matching claim-context pairs across disclosure channels. Implement and evaluate contradiction detection models using NLI frameworks and LLM prompting techniques. Aggregate model outputs into firm-quarter level scores and prepare structured datasets for empirical analysis. Support human-in-the-loop validation by coordinating with student annotators and preparing annotation guidelines. Maintain reproducible research workflows using version control (Git) and systematic documentation of processes, code, and findings. Job Requirements Bachelor’s degree or Master’s degree in Computer Science, Information Systems, Data Science, Artificial Intelligence, or a closely related field. Strong programming skills in Python, with experience in relevant libraries such as HuggingFace Transformers, PyTorch/TensorFlow, and scikit-learn. Prior experience or coursework in natural language processing, machine learning, or information retrieval. Familiarity with version control tools (e.g., Git/GitHub) and collaborative development practices. Experience with financial data or financial text analysis will be advantageous. Familiarity with LLM APIs, prompt engineering, and retrieval-augmented generation (RAG) workflows will be advantageous. Good communication skills and ability to work both independently and collaboratively.

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Lecturer in the Department of Pharmacology and Pharmacy

THE UNIVERSITY OF HONG KONG

The University of Hong Kong

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The University of Hong Kong

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Ref.: 536217 Work type: Full-time Department: Department of Pharmacology and Pharmacy (21300) Categories: Teaching Staff Applications are invited for appointment as Lecturer in the Department of Pharmacology and Pharmacy (Ref.: 536217), to commence as soon as possible on a three-year fixed-term basis, with the possibility of renewal subject to satisfactory performance. Applicants should possess (i) a Ph.D. degree in pharmaceutical sciences, chemistry, or allied sciences; (ii) work experience in the pharmaceutical industry or academia and (iii) at least 3 years’ teaching experience at undergraduate level. Preference will be given to those with experience in teaching medicinal chemistry, analytical chemistry, or pharmaceutical sciences. The appointee will deliver high-quality undergraduate teaching and assessments, including lectures and practical sessions. They will supervise research projects, mentor students, provide academic advising, contribute to curriculum development, and participate in knowledge transfer and collaborative research activities. A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits. At current rates, salaries tax does not exceed 15% of gross income. The appointment will attract a contract-end gratuity and University contribution to a retirement benefits scheme, totalling up to 15% of basic salary. The University only accepts online application for the above post. Applicants should apply online and upload an up-to-date C.V. Review of applications will start as soon as possible and continue until June 30, 2026, or until the post is filled, whichever is earlier.

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RMIT University

Australia

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1 x full time, fixed term (2 years) position available in the School of Science within STEM College Salary Academic Level A ($80,755 - $109,536) + 17% Superannuation Based at the City, but may be required to work and/or be based at other campuses of the University About the Role We are seeking an engineer to develop next-generation photonic devices using artificial intelligence, inverse design methods, and physics-based modelling. The role is embedded within the ARC E2Crop Hub and the Centre for Atomaterials and Nanomanufacturing (RMIT University), which focus on renewable energy systems, intelligent sensing, advanced materials, and nanoscale device technologies. The successful candidate will develop machine learning approaches (including generative models and physics-informed neural networks) to design and optimise integrated photonic devices and metasurfaces. This includes building automated workflows that link electromagnetic simulation tools with AI models to accelerate device discovery and performance optimisation. The role involves applying computational methods such as topology optimisation, adjoint techniques, and data-driven modelling to identify high-performance, manufacturable photonic structures while accounting for fabrication limits and multi-physics effects. You will also contribute to internal software tools that support rapid prototyping and enable broader research teams to use AI-driven design methods. The position offers access to advanced computational infrastructure and fabrication facilities, supporting research at the intersection of photonics, AI, and nanotechnology. About You Deep understanding of waveguide optics, light-matter interaction, and plasmonics Proficiency with electromagnetic simulation tools (e.g., Lumerical FDTD/MODE, COMSOL Multiphysics, Ansys HFSS, or Meep) Familiarity with Photonic Integrated device design flow and foundry PDKs (Silicon Photonics, SiN, or III-V) Strong proficiency in Python and machine learning frameworks (PyTorch, TensorFlow, or JAX) Experience with photonic design techniques and global optimisation algorithms (Genetic Algorithms, Particle Swarm, Gradient Descent) Experience implementing deep learning architectures relevant to physics, such as CNNs for image-based field prediction, Graph Neural Networks (GNNs), or Physics-Informed Neural Networks (PINNs) Solid grasp of numerical methods, partial differential equations (PDEs), and linear algebra Experience with high-performance computing (HPC) or cloud-based simulation acceleration Experience with integrated photonic device fabrication and light-matter interactions Evidence of research output including high quality publications, conference contributions and/or technical reports in the field. Ability to generate alternative funding projects through effective liaison with industry and government. Ability to work autonomously whilst displaying a strong commitment to work in a team environment, including the demonstrated ability to confidently and effectively work with colleagues, project team leaders, and industry partners. Demonstrated ability to meet deadlines and effectively manage varying workloads and respond to changing priorities as required. Demonstrated high level of communication skills. Qualifications Mandatory: Ph.D. degree in Optical engineering, Applied Physics, or Computer Science Publication record in top-tier journals, demonstrating the application of machine learning to physical problems Experience with surrogate modelling to replace computationally expensive simulations Knowledge of lithography limitations and design-for-manufacturability (DFM). Problem-solving abilities for determining innovative solutions to the complex problems common to photonics design and fabrication. Attention to detail for ensuring precision in coding and model training - and enhancing the accuracy and reliability of AI modelling and data quality. Collaborative skills for working effectively with data scientists, software developers, and project managers. Adaptability, including a willingness to stay updated with the rapidly evolving field of frontier research and learn new techniques and technologies as they emerge. Communication skills for explaining technical project requirements, progress, and outcomes to non-technical stakeholders Please Note: Appointment to this position is subject to passing a Working with Children and National Police Check. To Apply Please submit your CV and covering letter and address the Key Selection Criteria for this position by clicking on the 'Apply' link at the top of this page. For more information on this role, please connect with Dr. Mingdeng Luo, ARC E2Crop Hub Manager at mingdeng.luo2@rmit.edu.au Position Description - Research Assistant, AI Driven Photonics Design Engineer Please note, if you are viewing this advert from an external site, please click 'apply' and you will be redirected to RMIT's Jobs website to access the Position Description at the bottom of the page. Due to recent upgrades, position description links may not work on LinkedIn advertisements.Applications close on 14th of June, Sunday.About RMIT UniversityRMIT is a multi-sector university of technology, design and enterprise with more than 96,000 students and close to 10,000 staff globally. The University's mission is to help shape the world through research, innovation and engagement, and to create transformative experiences for students to prepare them for life and work.https://www.rmit.edu.au/abouthttps://www.universitiesaustralia.edu.au/university/rmit-universityWhy work at RMIT UniversityOur people make everything at the University possible. We encourage new approaches to work and learning, stimulating change to drive positive impact. Find out more about working at RMIT University, what we stand for and why we are an Employer of Choice.We want to attract those who will make a difference. View RMIT's impressive standings in university rankings.https://www.rmit.edu.au/about/facts-figures/reputation-and-rankingsBring Your Whole Self to WorkWe are better for our diversity. When we listen to those who are different to us, or challenge us, we grow stronger together. When we feel like we belong, we can achieve so much more. We respect each other, embrace our differences and build a sense of belonging in our team and beyond.We are proud that our dedication to diversity has been recognised by a broad spectrum of external organisations. Visit our Linkedin Life pages to learn more and see some of our most recent wins.RMIT has a strong dhumbali (commitment) to the employment, development and retention of Aboriginal and Torres Strait Islander people within a culturally safe environment; we strongly encourage applications from Aboriginal and Torres Strait Islander peoples.At RMIT, we are committed to supporting adjustments throughout the recruitment and selection process, as well as during employment. We actively support and encourage people with disability to apply to RMIT. To discuss adjustment requirements, please contact Dani (Senior Talent Advisor), via talentsupport@rmit.edu.au or visit our Careers page for more contact information - https://www.rmit.edu.au/careersRMIT is an equal opportunity employer committed to being a child safe organisation. We are dedicated to attracting, retaining and developing our people regardless of gender identity, ethnicity, sexual orientation, disability and age. Applications are encouraged from all sectors of the community and we strongly encourage applications from the Aboriginal and/or Torres Strait Islander community.We are a Circle Back Initiative Employer - we commit to respond to every applicant.

Salary

Competitive

Posted

14 May 2026

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