Total 3 Senior Executive level Education / Training job vacancies in Surabaya, East Java

About the Job

The Singapore University of Technology and Design (SUTD) is seeking a highly motivated and skilled Research Assistant to support a research project focused on leveraging multimodal Large Language Models (LLMs) to enhance user intention understanding and decision-making process, particularly in the area of recommender systems. The successful candidate will work closely with faculty and researchers to design, implement, and evaluate novel methods that integrate LLMs for more intelligent user understanding and decision-making systems. This position offers a unique opportunity to contribute to cutting-edge AI research with real-world impact.

What You'll Do

• Conduct research on applying LLMs to improve the user intent understanding and decision-making process.
• Develop and implement experimental prototypes and evaluate model performance.
• Conduct literature reviews and contribute to the writing of research papers and reports.
• Collaborate with team members and participate in regular research discussions.

Who We're Looking For

• A Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Proven research experience, particularly in user modeling, personalization and decision-making process and recommender systems.
• Strong programming skills (e.g., Python, PyTorch, TensorFlow).
• Excellent written English communication skills.
• Self-motivated, independent, and passionate about research.
• Familiarity with prompt engineering, fine-tuning LLMs, or knowledge distillation is a plus.
• Experience with publishing in academic conferences or journals is a plus.

Job description

Job Description & Responsibilities:

• Serve as the first point of contact for students, parents, and visitors, building trust and rapport.

• Handle customer enquiries, class registrations, and payments with attention to detail.

• Provide proactive problem-solving and issue resolution.

• Assist with day-to-day operations and administration, ensuring smooth and efficient branch function.

• Promote Aspire Hub’s mission and values, acting as a brand ambassador for our services.

• Ensure confidentiality when handling sensitive data (e.g., student records, payments).

• Strive for customer satisfaction, building strong relationships for increased retention and loyalty.

Job Requirements:

• Strong communication skills, with the ability to handle customer interactions professionally, empathetically, and with responsiveness.

• Ability to work a fixed schedule (1 PM – 10 PM) on weekday and (10AM-8PM) on weekend, with occasional flexibility depending on operational needs.

• Proactive, adaptable, and detail-oriented in handling tasks and resolving issues.

• Prior experience in customer service, sales, or administration is preferred, but candidates with the right attitude are encouraged to apply.

• Familiarity with Microsoft Office and CRM System for administrative tasks is required.

• Ability to maintain confidentiality and demonstrate ethical conduct in handling sensitive information.

Job Benefits:

• Formal training program to help you succeed.

• Collaborative, supportive work environment with room for growth.

• Opportunities for career development and internal promotions.

• Become part of a team dedicated to making a difference in education.

• Competitive salary package, incentive and performance bonuses

Job summary

Rewarding career in customer service with professional development and healthy work-life balance. Join our dynamic team!

• Attractive sales incentive
• Good career progression
• Competitive salary package and performance bonuses

Senior Research Fellow in Computational Modelling of Smart Knitted Textiles

We seek a Senior Research Fellow (3–5 years postdoctoral experience) to join a multidisciplinary team working on computational modelling of knitted textile structures for energy harvesting applications. The postholder will develop and apply advanced modelling and data-driven methods to establish quantitative links between textile architecture and functional performance, working in close collaboration with experimental colleagues.

Key Responsibilities

• Develop and validate computational models of knitted fabric structures, capturing the mechanical behaviour of complex knit architectures under various loading conditions.
• Establish quantitative relationships between textile structural parameters — such as stitch geometry, yarn properties, and knit pattern — and functional energy harvesting performance.
• Apply data-driven and machine learning techniques to accelerate model-based design and reduce reliance on physical prototyping.
• Validate model predictions against experimental data generated by collaborating groups and work closely with fabrication teams to inform design recommendations.
• Contribute to the broader research programme through independent scholarly initiative and collaboration with partner groups.
• Lead dissemination through high-impact journal publications and conference presentations.

Requirements

• PhD in mechanical engineering, materials science, computational mechanics, or a closely related discipline.
• 3–5 years of postdoctoral research experience with a strong publication record.
• Expertise in finite element modelling; experience with textile, fibre, or soft material mechanics is highly desirable.
• Familiarity with energy harvesting mechanisms (e.g., piezoelectric, triboelectric, or thermoelectric) is an advantage.
• Experience with machine learning or data-driven modelling methods is desirable.
• Ability to work independently and provide leadership to junior researchers and graduate students within an interdisciplinary team.
• Excellent written and oral communication skills in English.

About the Position

This is a full-time, 2-year position embedded in a flagship initiative on wearable-embedded embodied AI for healthy ageing and rehabilitation. The postholder will join tripartite collaboration team spanning advanced fiber design, knitting design and advanced signal processing expertise.