Aminur Rahman

Artificial Intelligence (AI) has emerged as one of the most sought-after academic disciplines worldwide, primarily due to its strong career prospects and expanding industrial applications. This study provides data-driven career guidance for prospective AI graduates by analysing recent labour market trends and examining the influence of geographic location on AI career opportunities. A structured dataset was analysed using a Random Forest regression model to forecast market trends and employment trajectories. Projections from the World Economic Forum’s 2025 report on emerging jobs towards 2030 were incorporated to provide a forward-looking perspective on AI employment dynamics. The findings indicate substantial growth in AI-related occupations, with net job growth for AI and Machine Learning roles projected to increase by approximately 82% by 2030. Among the most promising career paths are Data Scientists ([ ]113% projected growth), Machine Learning Specialists ([ ]82%), Software Engineers ([ ]57%), Cybersecurity Specialists ([ ]53%) and Robotics Engineers ([ ]40%). These results offer evidence-based insights to support informed academic and professional decision-making for students pursuing AI careers. These trends provide empirical support for strategic academic and workforce planning in AI-related fields. Students are encouraged to align their research and skill development with these domains, as AI is poised to become a central component of the future workforce.

Bangladesh has experienced substantial improvements in life expectancy over recent decades, yet persistent structural health risks continue to limit further gains in population longevity. This study examines the combined effects of malnutrition, air pollution, suicide mortality, non-communicable diseases (NCDs), maternal mortality, and road traffic injury mortality on life expectancy in Bangladesh using annual time-series data from 1990 to 2024. The analysis employs the Autoregressive Distributed Lag (ARDL) approach to assess both long-run equilibrium relationships and short-run dynamics, with robustness checks conducted using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) estimators. The findings confirm a stable long-run relationship between life expectancy and the selected health risk factors. In the long run, malnutrition, NCD prevalence, maternal mortality, and road traffic injury mortality significantly reduce life expectancy, highlighting the enduring influence of biological, systemic, and injury-related health constraints. Short-run results indicate a rapid adjustment toward long-run equilibrium following health shocks, suggesting that temporary interventions affect longevity without altering underlying structural outcomes. By integrating environmental, biological, and psychological dimensions within a unified empirical framework, this study contributes to the longevity literature in developing-country contexts and offers policy-relevant insights aligned with Sustainable Development Goal 3 on good health and well-being.

Instead of studying at a static point, this article has studied the dynamics of urban-rural wage disparities in Bangladesh by analyzing cross-sectional data derived from the labor force survey conducted during two distinct periods. The study utilizes the Juhn–Murphy–Pierce (JMP) decomposition method and extends it by adding propensity score matching (PSM) to derive empirical results. Moreover, the bootstrapping method determined the significance level of the empirical result. The findings of the study demonstrate that the pace of the reduction of the wage gap is impeded, mainly due to human capital and urban-centric industries. Based on the findings, it is recommended to prioritize investments in education and skills development and make policies to attract educated and skilled labor in rural areas. This paper provides informative details about the wage disparities between urban and rural areas in Bangladesh, which could be advantageous for academicians, researchers, planners, and policymakers.