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INESC TEC | Jurist – Public Procurement (AE2025-0288)

Published: 15 July 2025
Categories: Eng Jobs Academic Keys

RSS Feed Source: Academic Keys

Job ID: 259738

INESC TEC | Jurist – Public Procurement (AE2025-0288)
INESC TEC

Jurist – Public Procurement

Work description

The selected candidate will join the legal support team and the INESCTEC.OCEAN project, strengthening the public procurement area. They will be responsible for:

Analyze procedures for expenditure on goods and services. Initiating the contract formation procedures necessary for contracting goods, services, and works contracts. Preparing information, procedural documents, reports, and communications. Initiating direct award procedures, prior consultation, limited tender by prior qualification, public tender (national and international), and excluded contracting. Public procurement procedures through public procurement platforms, such as AcinGov and Anogov. – Assist in responding to public procurement procedures as the contracting entity. Be part of procedure juries. Ensure the external reporting relationships legally required in matters of public procurement. Propose

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Scholarships in STEM Network (S-STEM-Net)

Published: 14 July 2025
Categories: NSF Due Dates

RSS Feed Source: Academic Keys

On July 10, 2025, NSF issued an Important Notice providing updates to the agency’s research security policies, including a research security training requirement, Malign Foreign Talent Recruitment Program annual certification requirement, prohibition on Confucius institutes and an updated FFDR reporting and submission timeline.

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Civil Engineer (0480U), Facilities Services – 79632

Published: 14 July 2025
Categories: Eng Jobs Academic Keys

RSS Feed Source: Academic Keys

Civil Engineer (0480U), Facilities Services – 79632

About Berkeley

At the University of California, Berkeley, we are dedicated to fostering a community where everyone feels welcome and can thrive. Our culture of openness, freedom and belonging make it a special place for students, faculty and staff.

As a world-leading institution, Berkeley is known for its academic and research excellence, public mission, diverse student body, and commitment to equity and social justice. Since our founding in 1868, we have driven innovation, creating global intellectual, economic and social value.

We are looking for applicants who reflect California’s diversity and want to be part of an inclusive, equity-focused community that views education as a matter of social justice. Please consider whether your values align with our Guiding Values and Principles, Principles of Community, and Strategic Plan.

At UC Berkeley, we believe that learning is a fundamental part of working, and provide

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AI that delivers smarter glucose predictions without compromising privacy

Published: 14 July 2025
Categories: National Science Foundation

RSS Feed Source: Academic Keys

Managing diabetes is a daily challenge faced by nearly 40 million Americans. It involves tracking food intake, timing medication and engaging in physical activity. Getting it wrong can lead to serious health issues; therefore, developing better prediction tools is a vital part of effective diabetes care.

To support better diabetes management, researchers funded by multiple U.S. National Science Foundation grants are developing innovative tools that help patients predict blood sugar levels more precisely without compromising the privacy of their health data. This cutting-edge approach could transform how people with diabetes monitor and manage their condition in real-time.

At the core of this technology is a method called federated learning, which allows artificial intelligence models to be trained across many patients’ devices without sending any personal data to a central server. This setup is ideal for healthcare, where data privacy is paramount and patients often use battery- and memory-limited smart devices. But early federated learning systems struggled to adapt to individual differences, like how people eat, move or react to insulin.

To address this challenge, the research team grouped patients based on their carbohydrate (e.g., sugar and starch) intake levels. The idea is that people who eat in similar ways tend to show similar glucose patterns. By training the AI on these grouped behaviors, the model became more effective at making personalized blood glucose predictions.

To test

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